BioinspiredZephyr-7B: Large Language Model for the Mechanics of Biological and Bio-Inspired Materials

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.

The model is based on HuggingFaceH4/zephyr-7b-beta.

👁 image/png

This model is based on work reported in https://doi.org/10.1002/advs.202306724.

This repository includes both, Hugging Face transformers and GGUF files (in different versions, the q5_K_M is recommended).

Hugging Face transformers files: Loading and inference

from transformers import AutoModelForCausalLM, AutoTokenizer
from accelerate import infer_auto_device_map

model = AutoModelForCausalLM.from_pretrained(
 model_name,
 trust_remote_code=True,
 device_map="auto", #device_map="cuda:0",
 torch_dtype= torch.bfloat16,
 # use_flash_attention_2=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_name)

Chat template

messages = [
 {"role": "system", "content": "You are a friendly materials scientist."},
 {"role": "user", "content": "What is the strongest spider silk material?"},
 {"role": "assistant", "content": "Sample response."},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

'<|system|>\nYou are a friendly materials scientist.\n<|user|>\nWhat is the strongest spider silk material?\n<|assistant|>\nSample response.\n<|assistant|>\n'

device='cuda'
def generate_response (text_input="Biological materials offer amazing possibilities, such as",
 num_return_sequences=1,
 temperature=1., 
 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,
 ):

 inputs = tokenizer.encode(text_input, add_special_tokens =False, return_tensors ='pt')
 if verbatim:
 print ("Length of input, tokenized: ", inputs.shape, inputs)
 with torch.no_grad():
 outputs = model.generate(input_ids=inputs.to(device), 
 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,
 do_sample =True, 
 repetition_penalty=repetition_penalty,
 )
 return tokenizer.batch_decode(outputs[:,inputs.shape[1]:].detach().cpu().numpy(), skip_special_tokens=True)

Then:

messages = [
 {"role": "system", "content": "You are a friendly materials scientist."},
 {"role": "user", "content": "What is the strongest spider silk material?"},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

output_text=generate_response (text_input=prompt, eos_token_id=eos_token,
 num_return_sequences=1, repetition_penalty=1.,
 top_p=0.9, top_k=512, 
 temperature=0.1,max_new_tokens=512, verbatim=False,
 )
print (output_text)

GGUF files: Loading and inference

from llama_cpp import Llama

model_path='./BioinspiredZephyr-7B/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/BioinspiredZephyr-7B/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_BioinspiredZephyr_7B(system_prompt='You are an expert in biological materials, mechanics and related topics.',
 prompt="What is spider silk?",
 temperature=0.0,
 max_tokens=10000, 
 ):
 if system_prompt==None:
 messages=[
 {"role": "user", "content": prompt},
 ]
 else:
 messages=[
 {"role": "system", "content": system_prompt},
 {"role": "user", "content": prompt},
 ]

 result=llm.create_chat_completion(
 messages=messages,
 temperature=temperature,
 max_tokens=max_tokens,
 )

start_time = time.time()
result=generate_BioinspiredZephyr_7B(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)

arXiv: https://arxiv.org/abs/2309.08788