EdwardYu/llama-2-7b-MedQuAD

This model is fine-tuned on meta-llama/Llama-2-7b-chat-hf using MedQuAD (Medical Question Answering Dataset).
If you are interested how to fine-tune Llama-2 or other LLM models, the repo will tell you.

Usage

base_model = "meta-llama/Llama-2-7b-chat-hf"
adapter = 'EdwardYu/llama-2-7b-MedQuAD'

tokenizer = AutoTokenizer.from_pretrained(adapter)

model = AutoModelForCausalLM.from_pretrained(
    base_model,
    load_in_4bit=True,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.bfloat16,
        bnb_4bit_use_double_quant=True,
        bnb_4bit_quant_type='nf4'
    ),
)
model = PeftModel.from_pretrained(model, adapter)

question = 'What are the side effects or risks of Glucagon?'
inputs = tokenizer(question, return_tensors="pt").to("cuda")
outputs = model.generate(inputs=inputs.input_ids, max_length=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

To run model inference faster, you can load in 16-bits without 4-bit quantization.

model = AutoModelForCausalLM.from_pretrained(
    base_model,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter)