---
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.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/bsqBByauWBZ0Y8PspthR8.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.</s>\n<|user|>\nWhat is the strongest spider silk material?</s>\n<|assistant|>\nSample response.</s>\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)
```