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モデル間の重みの加減算のみで構築した日本語LLM • 3 items • Updated • 2
How to use ryota39/Tora-7B-v0.2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="ryota39/Tora-7B-v0.2") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("ryota39/Tora-7B-v0.2")
model = AutoModelForCausalLM.from_pretrained("ryota39/Tora-7B-v0.2")How to use ryota39/Tora-7B-v0.2 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ryota39/Tora-7B-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ryota39/Tora-7B-v0.2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/ryota39/Tora-7B-v0.2
How to use ryota39/Tora-7B-v0.2 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "ryota39/Tora-7B-v0.2" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ryota39/Tora-7B-v0.2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "ryota39/Tora-7B-v0.2" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ryota39/Tora-7B-v0.2",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use ryota39/Tora-7B-v0.2 with Docker Model Runner:
docker model run hf.co/ryota39/Tora-7B-v0.2
非商用ライセンスで公開します。
Tora-7B-v0.2 = NTQAI/chatntq-ja-7b-v1.0 + (NousResearch/Hermes-2-Pro-Mistral-7B - mistralai/Mistral-7B-v0.1)
@jovyan様の実装を参考に下記のコードでモデルを作成しました。
import torch
from transformers import AutoModelForCausalLM
def build_chat_vector_model(
base_model_name,
inst_model_name,
target_model_name,
skip_layers,
):
base_model = AutoModelForCausalLM.from_pretrained(
base_model_name,
torch_dtype=torch.bfloat16,
device_map="cpu",
)
inst_model = AutoModelForCausalLM.from_pretrained(
inst_model_name,
torch_dtype=torch.bfloat16,
device_map="cpu",
)
target_model = AutoModelForCausalLM.from_pretrained(
target_model_name,
torch_dtype=torch.bfloat16,
device_map="cuda",
)
# 英語ベースモデル
for k, v in base_model.state_dict().items():
print(k, v.shape)
# 日本語継続事前学習モデル
for k, v in target_model.state_dict().items():
print(k, v.shape)
# 除外対象
skip_layers = ["model.embed_tokens.weight", "lm_head.weight"]
for k, v in target_model.state_dict().items():
# layernormも除外
if (k in skip_layers) or ("layernorm" in k):
continue
chat_vector = inst_model.state_dict()[k] - base_model.state_dict()[k]
new_v = v + chat_vector.to(v.device)
v.copy_(new_v)
target_model.save_pretrained("./chat_model")
return
if __name__ == '__main__':
base_model_name = "mistralai/Mistral-7B-v0.1"
inst_model_name = "NousResearch/Hermes-2-Pro-Mistral-7B"
target_model_name = "NTQAI/chatntq-ja-7b-v1.0"
skip_layers = ["model.embed_tokens.weight", "lm_head.weight"]
build_chat_vector_model(
base_model_name=base_model_name,
inst_model_name=inst_model_name,
target_model_name=target_model_name,
skip_layers=skip_layers
)
| model | category | score | ver |
|---|---|---|---|
| Tora-7B-v0.2 | Writing | 3.8 | single-turn |
| Tora-7B-v0.2 | Roleplay | 7.1 | single-turn |
| Tora-7B-v0.2 | Reasoning | 6.3 | single-turn |
| Tora-7B-v0.2 | Math | 3.0 | single-turn |
| Tora-7B-v0.2 | Coding | 2.2 | single-turn |
| Tora-7B-v0.2 | Extraction | 6.6 | single-turn |
| Tora-7B-v0.2 | STEM | 7.2 | single-turn |
| Tora-7B-v0.2 | Humanities | 8.2 | single-turn |
ChatVectorの記事を執筆してくださった@jovyan様に深くお礼申し上げます。