metadata
license: apache-2.0
BioinspiredMixtral: Large Language Model for the Mechanics of Biological and Bio-Inspired Materials using Mixture-of-Experts
To accelerate discovery and guide insights, we report an open-source autoregressive transformer large language model (LLM), trained on expert knowledge in the biological materials field, especially focused on mechanics and structural properties.
The model is finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity.
This model is based on work reported in https://doi.org/10.1002/advs.202306724, but focused on the development of a mixture-of-experts strategy.
The model is a fine-tuned version of mistralai/Mixtral-8x7B-Instruct-v0.1.
from llama_cpp import Llama
model_path='lamm-mit/BioinspiredMixtral/ggml-model-q5_K_M.gguf'
chat_format="mistral-instruct"
llm = Llama(model_path=model_path,
n_gpu_layers=-1,verbose= True,
n_ctx=10000,
#main_gpu=0,
chat_format=chat_format,
#split_mode=llama_cpp.LLAMA_SPLIT_LAYER
)
Or, download directly from Hugging Face:
from llama_cpp import Llama
model_path='lamm-mit/BioinspiredMixtral/ggml-model-q5_K_M.gguf'
chat_format="mistral-instruct"
llm = Llama.from_pretrained(
repo_id=model_path,
filename="*q5_K_M.gguf",
verbose=True,
n_gpu_layers=-1,
n_ctx=10000,
#main_gpu=0,
chat_format=chat_format,
)
For inference:
def generate_response (model,tokenizer,text_input="Biology offers amazing possibilities, especially for",
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.,eos_token_id=2,verbatim=False,
exponential_decay_length_penalty_fac=None,add_special_tokens =True,
):
inputs = tokenizer(text_input, add_special_tokens = add_special_tokens, return_tensors ='pt').to(device)
with torch.no_grad():
outputs = model.generate (input_ids = inputs["input_ids"],
attention_mask = inputs["attention_mask"] , # This is usually done automatically by the tokenizer
max_new_tokens=max_new_tokens,
temperature=temperature, #value used to modulate the next token probabilities.
num_beams=num_beams,
top_k = top_k,
top_p = top_p,
num_return_sequences = num_return_sequences,
eos_token_id=eos_token_id,
pad_token_id = eos_token_id,
do_sample =True,#skip_prompt=True,
repetition_penalty=repetition_penalty,
)
return tokenizer.batch_decode(outputs[:,inputs["input_ids"].shape[1]:].detach().cpu().numpy(), skip_special_tokens=True)
def generate_BioMixtral (system_prompt='You a helpful assistant. You are familiar with materials science, especially biological and bioinspired materials. ',
prompt='What is spider silk in the context of bioinspired materials?',
repetition_penalty=1.,
top_p=0.9, top_k=256,
temperature=0.5, max_tokens=512, verbatim=False, eos_token=None,
prepend_response='',
):
if eos_token==None:
eos_token= tokenizer.eos_token_id
if system_prompt==None:
messages=[
{"role": "user", "content": prompt},
]
else:
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt},
]
txt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True,
)
txt=txt+prepend_response
output_text=generate_response (model,tokenizer,text_input=txt,eos_token_id=eos_token,
num_return_sequences=1, repetition_penalty=repetition_penalty,
top_p=top_p, top_k=top_k,
temperature=temperature,max_new_tokens=max_tokens, verbatim=verbatim,
)
return output_text[0]
start_time = time.time()
result=generate_BioMixtral(system_prompt='You respond accurately.',
prompt="What is graphene? Answer with detail.",
max_tokens=512, temperature=0.7, )
print (result)
deltat=time.time() - start_time
print("--- %s seconds ---" % deltat)
toked=tokenizer(res)
print ("Tokens per second (generation): ", len (toked['input_ids'])/deltat)