Inference example
model_name='lamm-mit/BioinspiredLlama-3-1-8B-128k'
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
device_map="auto",
torch_dtype =torch.bfloat16,
attn_implementation="flash_attention_2"
)
model.config.use_cache = True
tokenizer = AutoTokenizer.from_pretrained(model_name)
Check https://huggingface.co/lamm-mit/BioinspiredLlama-3-1-8B-128k-dominant-protein-SS-structure for further examples use of the model for protein structural features prediction, including a fine-tuning script.
Function to interact with the model
def generate_response (text_input="What is spider silk?",
system_prompt='',
num_return_sequences=1,
temperature=1., #the higher the temperature, the more creative the model becomes
max_new_tokens=127,device='cuda',
add_special_tokens = False, #since tokenizer.apply_chat_template adds <|begin_of_text|> template already, set to False
num_beams=1,eos_token_id= [
128001,
128008,
128009
], verbatim=False,
top_k = 50,
top_p = 0.9,
repetition_penalty=1.1,
messages=[],
):
if messages==[]: #start new messages dictionary
if system_prompt != '': #include system prompt if provided
messages.extend ([ {"role": "system", "content": system_prompt}, ])
messages.extend ( [ {"role": "user", "content": text_input}, ])
else: #if messages provided, will extend (make sure to add previous response as assistant message)
messages.append ({"role": "user", "content": text_input})
text_input = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = tokenizer([text_input], add_special_tokens = add_special_tokens, return_tensors ='pt' ).to(device)
if verbatim:
print (inputs)
with torch.no_grad():
outputs = model.generate(**inputs,
max_new_tokens=max_new_tokens,
temperature=temperature,
num_beams=num_beams,
top_k = top_k,eos_token_id=eos_token_id,
top_p =top_p,
num_return_sequences = num_return_sequences,
do_sample =True, repetition_penalty=repetition_penalty,
)
outputs=outputs[:, inputs["input_ids"].shape[1]:]
return tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True), messages
Usage:
res,_= generate_response (text_input = "What is collagen?", system_prompt = 'You are a materials scientist.',
num_return_sequences=1,
temperature=1., #the higher the temperature, the more creative the model becomes
max_new_tokens=127,
num_beams=1,
top_k = 50, top_p =0.9, repetition_penalty=1.1,
)
print (res[0])
To realize multi-turn interactions, see this example:
res, messages = generate_response (text_input="What is spider silk?", messages=[])
messages.append ({"role": "assistant", "content": res[0]}, ) #append result to messages dict
print (res)
res, messages = generate_response (text_input="Explain this result in detail.", messages=messages)
messages.append ({"role": "assistant", "content": res[0]}, ) #append result to messages dict
print (res)
res, messages = generate_response (text_input="Provide this in JSON format.", messages=messages)
messages.append ({"role": "assistant", "content": res[0]}) #append result to messages dict
print (res)
Fine-tuning script
See (https://huggingface.co/lamm-mit/BioinspiredLlama-3-1-8B-128k-dominant-protein-SS-structure) for a Colab fine-tuning script.