Llama3-8B-SFT-SyntheticMedical-bnb-4bit

Model Details

Model Description

Llama3-8B-SFT-SSyntheticMedical-bnb-4bit is trained using the SFT method via QLoRA on 4336 rows of medical data to enhance it's abilities in the realm of scientific anatomy.

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Using the model with transformers

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_name_or_path = "thesven/Llama3-8B-SFT-SyntheticMedical-bnb-4bit"

# BitsAndBytesConfig for loading the model in 4-bit precision
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="float16",
)

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name_or_path,
    device_map="auto",
    trust_remote_code=False,
    revision="main",
    quantization_config=bnb_config
)
model.pad_token = model.config.eos_token_id

prompt_template = '''
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are an expert in the field of anatomy, help explain its topics to me.<|eot_id|><|start_header_id|>user<|end_header_id|>

What is the function of the hamstring?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
'''

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.1, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)

print(generated_text)