⚠️⚠️⚠️ Only for research purpose. Do not use it for medical purpose. ⚠️⚠️⚠️
MedSwallow-70B🏥
東工大Swallowをベースモデルとし, 医療Q&AデータセットでInstruction Tuningを施した医療ドメインの日本語LLMです.
チューニングには独自で用意した米国医師国家試験(USMLE)を和訳したQ&Aデータセットを用いました.
MedSwallow is a Japanese medical LLM for medical question-answering.
MedSwallow is based on Swallow-70B and has passed instruction tuning with USMLE dataset translated in Japanese by our own.
Training procedure
The following bitsandbytes
quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
Framework versions
- PEFT 0.4.0
License
ライセンスは非商用ライセンスです.
Non-commercial.
Usage
model_name = "tokyotech-llm/Swallow-70b-instruct-hf"
peft_model= "AIgroup-CVM-utokyohospital/MedSwallow-70b"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
load_in_8bit=False,
torch_dtype=torch.float16,
device_map=device,
model = PeftModel.from_pretrained(
model,
peft_model,
torch_dtype=torch.float16,
device_map=device,
)
Benchmark
See also Japanese Medical Language Model Evaluation Harness.
- IgakuQA (in English):
- IgakuQA (in Japanese):
- MedQA (in English) :
- MedQA (in Japanese) :
How to cite
coming soon...
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