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Model: lamm-mit/BioinspiredZephyr-7B Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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---
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### BioinspiredZephyr-7B: Large Language Model for the Mechanics of Biological and Bio-Inspired Materials
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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.
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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.
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The model is based on HuggingFaceH4/zephyr-7b-beta.
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This model is based on work reported in https://doi.org/10.1002/advs.202306724.
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This repository includes both, Hugging Face transformers and GGUF files (in different versions, the q5_K_M is recommended).
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#### Hugging Face transformers files: Loading and inference
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```
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from accelerate import infer_auto_device_map
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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trust_remote_code=True,
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device_map="auto", #device_map="cuda:0",
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torch_dtype= torch.bfloat16,
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# use_flash_attention_2=True,
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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```
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Chat template
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```
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messages = [
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{"role": "system", "content": "You are a friendly materials scientist."},
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{"role": "user", "content": "What is the strongest spider silk material?"},
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{"role": "assistant", "content": "Sample response."},
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]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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```
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'<|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'
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```
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device='cuda'
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def generate_response (text_input="Biological materials offer amazing possibilities, such as",
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num_return_sequences=1,
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temperature=1.,
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max_new_tokens=127,
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num_beams=1,
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top_k = 50,
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top_p =0.9,repetition_penalty=1.,eos_token_id=2,verbatim=False,
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exponential_decay_length_penalty_fac=None,
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):
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inputs = tokenizer.encode(text_input, add_special_tokens =False, return_tensors ='pt')
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if verbatim:
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print ("Length of input, tokenized: ", inputs.shape, inputs)
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with torch.no_grad():
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outputs = model.generate(input_ids=inputs.to(device),
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max_new_tokens=max_new_tokens,
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temperature=temperature, #value used to modulate the next token probabilities.
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num_beams=num_beams,
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top_k = top_k,
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top_p =top_p,
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num_return_sequences = num_return_sequences, eos_token_id=eos_token_id,
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do_sample =True,
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repetition_penalty=repetition_penalty,
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)
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return tokenizer.batch_decode(outputs[:,inputs.shape[1]:].detach().cpu().numpy(), skip_special_tokens=True)
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```
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Then:
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```
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messages = [
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{"role": "system", "content": "You are a friendly materials scientist."},
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{"role": "user", "content": "What is the strongest spider silk material?"},
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]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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output_text=generate_response (text_input=prompt, eos_token_id=eos_token,
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num_return_sequences=1, repetition_penalty=1.,
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top_p=0.9, top_k=512,
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temperature=0.1,max_new_tokens=512, verbatim=False,
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)
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print (output_text)
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```
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#### GGUF files: Loading and inference
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```
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from llama_cpp import Llama
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model_path='./BioinspiredZephyr-7B/ggml-model-q5_K_M.gguf'
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chat_format="mistral-instruct"
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llm = Llama(model_path=model_path,
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n_gpu_layers=-1,verbose= True,
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n_ctx=10000,
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#main_gpu=0,
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chat_format=chat_format,
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#split_mode=llama_cpp.LLAMA_SPLIT_LAYER
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)
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```
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Or, download directly from Hugging Face:
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```
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from llama_cpp import Llama
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model_path='lamm-mit/BioinspiredZephyr-7B/ggml-model-q5_K_M.gguf'
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chat_format="mistral-instruct"
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llm = Llama.from_pretrained(
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repo_id=model_path,
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filename="*q5_K_M.gguf",
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verbose=True,
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n_gpu_layers=-1,
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n_ctx=10000,
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#main_gpu=0,
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chat_format=chat_format,
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)
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```
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For inference:
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```
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def generate_BioinspiredZephyr_7B(system_prompt='You are an expert in biological materials, mechanics and related topics.',
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prompt="What is spider silk?",
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temperature=0.0,
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max_tokens=10000,
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):
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if system_prompt==None:
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messages=[
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{"role": "user", "content": prompt},
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]
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else:
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": prompt},
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]
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result=llm.create_chat_completion(
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messages=messages,
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temperature=temperature,
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max_tokens=max_tokens,
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)
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start_time = time.time()
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result=generate_BioinspiredZephyr_7B(system_prompt='You respond accurately.',
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prompt="What is graphene? Answer with detail.",
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max_tokens=512, temperature=0.7, )
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print (result)
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deltat=time.time() - start_time
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print("--- %s seconds ---" % deltat)
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toked=tokenizer(res)
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print ("Tokens per second (generation): ", len (toked['input_ids'])/deltat)
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```
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arXiv: https://arxiv.org/abs/2309.08788
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