AKK 60m

Name: AKK 60m
Author: Thalesian

2 languages

license:apache-2.0

Thalesian

Language Model

OTHER

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Quick Summary

AI model with specialized capabilities.

Code Examples

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

Base modelpythontransformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_path = "Thalesian/AKK-60m"
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, local_files_only=True)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# 1) Prepare your cuneiform input
prompt = "Translate Akkadian cuneiform to English: "
input_text = "𒄨 𒃼 𒁺 𒊭 𒀸 𒌅 𒆰 𒋾 𒀸 𒋩 𒂗 𒋙 𒆰 𒆳 𒆷 𒈠 𒄀 𒊑 𒋗 𒁶 𒋻 𒁁 𒋾 𒌑 𒁖 𒆥 𒄣 𒀀 𒁍 𒄫 𒄑 𒁍 𒉡 𒈠 𒍣 𒆥 𒆧 𒅎 𒉡 𒌋 "

# 2) Tokenize & get model outputs
inputs = tokenizer(prompt + input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=64)

# 3) Decode prediction
prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Reference:", "young man valiant who through help assur lord all of not submissive one like a pottery bowl crush minutely like a flood flatten as nothing count")
print("Prediction:", prediction)

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