license: apache-2.0
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.
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)