Model summary

• instruction-tuning on medical data based on LLaMA

data

• Common
  • alpaca-5.2k
  • unatural-instruct 80k
  • OIG-40M
• Chinese
  • english/chinese translation data
  • zhihu QA
  • pCLUE
• Medical Domain:
  • MedDialog-200k
  • Chinese-medical-dialogue-data
  • WebMedQA
• code
  • alpaca_code-20k

training

Model

• LLaMA-7B

Hardware

• 6 x A100 40G using NVLink 4 inter-gpu connects

Software

• tokenizers==0.12.1
• sentencepiece==0.1.97
• transformers==4.28
• torch==2.0.0+cu117

How to use

import torch
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
from peft import PeftModel

base_model="llma-7b"
LORA_WEIGHTS = "llma-med-alpaca-7b"
LOAD_8BIT = False

tokenizer = LlamaTokenizer.from_pretrained(base_model)

model = LlamaForCausalLM.from_pretrained(
    base_model
    load_in_8bit=LOAD_8BIT,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(
    model,
    LORA_WEIGHTS,
    torch_dtype=torch.float16,
)

config = {
    "temperature": 0 ,
    "max_new_tokens": 1024,
    "top_p": 0.5
}

prompt = "Translate to English: Je t’aime."
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
outputs = model.generate(input_ids=input_ids, max_new_tokens=config["max_new_tokens"], temperature=config["temperature"])
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
print(decoded[len(prompt):])

Limitations

• This model may output harmful, biased, toxic, and illusory things, and currently does not undergo RLHF training, so this model is only for research purposes

TODO

• [x] self-instruct data
• [x] english medical data
• [ ] code data
• [ ] chinese corpus/medical dialog data

Reference

• LLaMA: Open and Efficient Foundation Language Models
• Alpaca: A strong open-source instruction-following model