LLM-wsd-FT-20000

1

5 languages

llama

by

swap-uniba

Language Model

OTHER

2503.08662B params

New

1 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

5595GB+ RAM

Mobile

Laptop

Server

Quick Summary

LLM-wsd-FT-20000 is a Large Language Model (LLM) instruction-tuned over meta-llama/Meta-Llama-3.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

2332GB+ RAM

Code Examples

How to Get Started with the Modelpythontransformers

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.trainer_utils import set_seed

target_word = "long"
instruction = f"Give a brief definition of the word \"{target_word}\" in the sentence given as input. Generate only the definition."
input_sentence = "How long has it been since you reviewed the objectives of your benefit and service program?"

model_id = "swap-uniba/LLM-wsd-FT-20000"

set_seed(42)

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)

tokenizer.padding_side = "left"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map='cuda',
    torch_dtype=torch.bfloat16,
).eval()

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

messages = [
    {"role": "user", "content": instruction + " Input: \"" + input_sentence + "\""},
]

input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(
    input_ids.to('cuda'),
    max_new_tokens=512,
    eos_token_id=terminators,
    num_beams=1,
    do_sample=False
)

print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

How to Get Started with the Modelpythontransformers

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.trainer_utils import set_seed

target_word = "long"
instruction = f"Give a brief definition of the word \"{target_word}\" in the sentence given as input. Generate only the definition."
input_sentence = "How long has it been since you reviewed the objectives of your benefit and service program?"

model_id = "swap-uniba/LLM-wsd-FT-20000"

set_seed(42)

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)

tokenizer.padding_side = "left"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map='cuda',
    torch_dtype=torch.bfloat16,
).eval()

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

messages = [
    {"role": "user", "content": instruction + " Input: \"" + input_sentence + "\""},
]

input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(
    input_ids.to('cuda'),
    max_new_tokens=512,
    eos_token_id=terminators,
    num_beams=1,
    do_sample=False
)

print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

How to Get Started with the Modelpythontransformers

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.trainer_utils import set_seed

target_word = "long"
instruction = f"Give a brief definition of the word \"{target_word}\" in the sentence given as input. Generate only the definition."
input_sentence = "How long has it been since you reviewed the objectives of your benefit and service program?"

model_id = "swap-uniba/LLM-wsd-FT-20000"

set_seed(42)

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)

tokenizer.padding_side = "left"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map='cuda',
    torch_dtype=torch.bfloat16,
).eval()

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

messages = [
    {"role": "user", "content": instruction + " Input: \"" + input_sentence + "\""},
]

input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(
    input_ids.to('cuda'),
    max_new_tokens=512,
    eos_token_id=terminators,
    num_beams=1,
    do_sample=False
)

print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

How to Get Started with the Modelpythontransformers

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.trainer_utils import set_seed

target_word = "long"
instruction = f"Give a brief definition of the word \"{target_word}\" in the sentence given as input. Generate only the definition."
input_sentence = "How long has it been since you reviewed the objectives of your benefit and service program?"

model_id = "swap-uniba/LLM-wsd-FT-20000"

set_seed(42)

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)

tokenizer.padding_side = "left"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map='cuda',
    torch_dtype=torch.bfloat16,
).eval()

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

messages = [
    {"role": "user", "content": instruction + " Input: \"" + input_sentence + "\""},
]

input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(
    input_ids.to('cuda'),
    max_new_tokens=512,
    eos_token_id=terminators,
    num_beams=1,
    do_sample=False
)

print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

How to Get Started with the Modelpythontransformers

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.trainer_utils import set_seed

target_word = "long"
instruction = f"Give a brief definition of the word \"{target_word}\" in the sentence given as input. Generate only the definition."
input_sentence = "How long has it been since you reviewed the objectives of your benefit and service program?"

model_id = "swap-uniba/LLM-wsd-FT-20000"

set_seed(42)

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)

tokenizer.padding_side = "left"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map='cuda',
    torch_dtype=torch.bfloat16,
).eval()

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

messages = [
    {"role": "user", "content": instruction + " Input: \"" + input_sentence + "\""},
]

input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(
    input_ids.to('cuda'),
    max_new_tokens=512,
    eos_token_id=terminators,
    num_beams=1,
    do_sample=False
)

print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

How to Get Started with the Modelpythontransformers

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.trainer_utils import set_seed

target_word = "long"
instruction = f"Give a brief definition of the word \"{target_word}\" in the sentence given as input. Generate only the definition."
input_sentence = "How long has it been since you reviewed the objectives of your benefit and service program?"

model_id = "swap-uniba/LLM-wsd-FT-20000"

set_seed(42)

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)

tokenizer.padding_side = "left"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map='cuda',
    torch_dtype=torch.bfloat16,
).eval()

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

messages = [
    {"role": "user", "content": instruction + " Input: \"" + input_sentence + "\""},
]

input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(
    input_ids.to('cuda'),
    max_new_tokens=512,
    eos_token_id=terminators,
    num_beams=1,
    do_sample=False
)

print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

How to Get Started with the Modelpythontransformers

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.trainer_utils import set_seed

target_word = "long"
instruction = f"Give a brief definition of the word \"{target_word}\" in the sentence given as input. Generate only the definition."
input_sentence = "How long has it been since you reviewed the objectives of your benefit and service program?"

model_id = "swap-uniba/LLM-wsd-FT-20000"

set_seed(42)

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)

tokenizer.padding_side = "left"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map='cuda',
    torch_dtype=torch.bfloat16,
).eval()

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

messages = [
    {"role": "user", "content": instruction + " Input: \"" + input_sentence + "\""},
]

input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(
    input_ids.to('cuda'),
    max_new_tokens=512,
    eos_token_id=terminators,
    num_beams=1,
    do_sample=False
)

print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

How to Get Started with the Modelpythontransformers

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.trainer_utils import set_seed

target_word = "long"
instruction = f"Give a brief definition of the word \"{target_word}\" in the sentence given as input. Generate only the definition."
input_sentence = "How long has it been since you reviewed the objectives of your benefit and service program?"

model_id = "swap-uniba/LLM-wsd-FT-20000"

set_seed(42)

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)

tokenizer.padding_side = "left"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map='cuda',
    torch_dtype=torch.bfloat16,
).eval()

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

messages = [
    {"role": "user", "content": instruction + " Input: \"" + input_sentence + "\""},
]

input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(
    input_ids.to('cuda'),
    max_new_tokens=512,
    eos_token_id=terminators,
    num_beams=1,
    do_sample=False
)

print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

How to Get Started with the Modelpythontransformers

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.trainer_utils import set_seed

target_word = "long"
instruction = f"Give a brief definition of the word \"{target_word}\" in the sentence given as input. Generate only the definition."
input_sentence = "How long has it been since you reviewed the objectives of your benefit and service program?"

model_id = "swap-uniba/LLM-wsd-FT-20000"

set_seed(42)

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)

tokenizer.padding_side = "left"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map='cuda',
    torch_dtype=torch.bfloat16,
).eval()

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

messages = [
    {"role": "user", "content": instruction + " Input: \"" + input_sentence + "\""},
]

input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(
    input_ids.to('cuda'),
    max_new_tokens=512,
    eos_token_id=terminators,
    num_beams=1,
    do_sample=False
)

print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

How to Get Started with the Modelpythontransformers

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.trainer_utils import set_seed

target_word = "long"
instruction = f"Give a brief definition of the word \"{target_word}\" in the sentence given as input. Generate only the definition."
input_sentence = "How long has it been since you reviewed the objectives of your benefit and service program?"

model_id = "swap-uniba/LLM-wsd-FT-20000"

set_seed(42)

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)

tokenizer.padding_side = "left"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map='cuda',
    torch_dtype=torch.bfloat16,
).eval()

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

messages = [
    {"role": "user", "content": instruction + " Input: \"" + input_sentence + "\""},
]

input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(
    input_ids.to('cuda'),
    max_new_tokens=512,
    eos_token_id=terminators,
    num_beams=1,
    do_sample=False
)

print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

How to Get Started with the Modelpythontransformers

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.trainer_utils import set_seed

target_word = "long"
instruction = f"Give a brief definition of the word \"{target_word}\" in the sentence given as input. Generate only the definition."
input_sentence = "How long has it been since you reviewed the objectives of your benefit and service program?"

model_id = "swap-uniba/LLM-wsd-FT-20000"

set_seed(42)

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)

tokenizer.padding_side = "left"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map='cuda',
    torch_dtype=torch.bfloat16,
).eval()

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

messages = [
    {"role": "user", "content": instruction + " Input: \"" + input_sentence + "\""},
]

input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(
    input_ids.to('cuda'),
    max_new_tokens=512,
    eos_token_id=terminators,
    num_beams=1,
    do_sample=False
)

print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

How to Get Started with the Modelpythontransformers

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.trainer_utils import set_seed

target_word = "long"
instruction = f"Give a brief definition of the word \"{target_word}\" in the sentence given as input. Generate only the definition."
input_sentence = "How long has it been since you reviewed the objectives of your benefit and service program?"

model_id = "swap-uniba/LLM-wsd-FT-20000"

set_seed(42)

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)

tokenizer.padding_side = "left"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map='cuda',
    torch_dtype=torch.bfloat16,
).eval()

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

messages = [
    {"role": "user", "content": instruction + " Input: \"" + input_sentence + "\""},
]

input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(
    input_ids.to('cuda'),
    max_new_tokens=512,
    eos_token_id=terminators,
    num_beams=1,
    do_sample=False
)

print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

How to Get Started with the Modelpythontransformers

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.trainer_utils import set_seed

target_word = "long"
instruction = f"Give a brief definition of the word \"{target_word}\" in the sentence given as input. Generate only the definition."
input_sentence = "How long has it been since you reviewed the objectives of your benefit and service program?"

model_id = "swap-uniba/LLM-wsd-FT-20000"

set_seed(42)

tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)

tokenizer.padding_side = "left"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map='cuda',
    torch_dtype=torch.bfloat16,
).eval()

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

messages = [
    {"role": "user", "content": instruction + " Input: \"" + input_sentence + "\""},
]

input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")

outputs = model.generate(
    input_ids.to('cuda'),
    max_new_tokens=512,
    eos_token_id=terminators,
    num_beams=1,
    do_sample=False
)

print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

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