sarashina2-7b-abliterated
24
1
7.0B
2 languages
llama
by
ronantakizawa
Language Model
OTHER
7B params
New
24 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
16GB+ RAM
Mobile
Laptop
Server
Quick Summary
This is an abliterated (refusal-removed) version of sbintuitions/sarashina2-7b.
Device Compatibility
Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
7GB+ RAM
Code Examples
Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Usagepythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "ronantakizawa/sarashina2-7b-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
messages = [
{"role": "user", "content": "こんにちは"}
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))Deploy This Model
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