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Model: lamm-mit/BioinspiredZephyr-7B
Source: Original Platform

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README.md

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+---
+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)
+```
+
+arXiv: https://arxiv.org/abs/2309.08788

config.json

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+{
+  "_name_or_path": "HuggingFaceH4/zephyr-7b-beta",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
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+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 2,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.37.0.dev0",
+  "use_cache": false,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.37.0.dev0"
+}

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special_tokens_map.json

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+{
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+    "<unk>",
+    "<s>",
+    "</s>"
+  ],
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "eos_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
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+  "add_eos_token": false,
+  "added_tokens_decoder": {
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+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "normalized": false,
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+      "single_word": false,
+      "special": true
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
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+  "additional_special_tokens": [
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+    "</s>"
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+  "bos_token": "<s>",
+  "chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n'  + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "device_map": "auto",
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "truncation_side": "left",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": true
+}