lamm-mit
/

Bioinspired-Llama-3-1-8B-128k-gamma

@@ -1,199 +1,104 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
 library_name: transformers
+tags:
+- biology
+- chemistry
+- biological materials
+- materials science
+- engineering
+- materials informatics
+- scientific AI
+- AI4science
+- Llama-3-1
 ---
+## Inference example
+```
+model_name='lamm-mit/Bioinspired-Llama-3-1-8B-128k-gamma'
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+    device_map="auto",
+    torch_dtype =torch.bfloat16,
+    attn_implementation="flash_attention_2"
+)
+model.config.use_cache = True
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+```
+#### Function to interact with the model
+```
+def generate_response (text_input="What is spider silk?",
+                       system_prompt='',
+                       num_return_sequences=1,
+                       temperature=1., #the higher the temperature, the more creative the model becomes
+                       max_new_tokens=127,device='cuda',
+                       add_special_tokens = False, #since tokenizer.apply_chat_template adds <|begin_of_text|> template already, set to False
+                       num_beams=1,eos_token_id= [
+                                            128001,
+                                            128008,
+                                            128009
+                                          ], verbatim=False,
+                       top_k = 50,
+                       top_p = 0.9,
+                       repetition_penalty=1.1,
+                       messages=[],
+                      ):
+    if messages==[]: #start new messages dictionary
+        if system_prompt != '': #include system prompt if provided
+            messages.extend ([  {"role": "system", "content": system_prompt},  ])
+        messages.extend ( [   {"role": "user", "content": text_input}, ])
+    else: #if messages provided, will extend (make sure to add previous response as assistant message)
+        messages.append ({"role": "user", "content": text_input})
+    text_input = tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+    )
+    inputs = tokenizer([text_input],  add_special_tokens = add_special_tokens,  return_tensors ='pt' ).to(device)
+    if verbatim:
+        print (inputs)
+    with torch.no_grad():
+          outputs = model.generate(**inputs,
+                                   max_new_tokens=max_new_tokens,
+                                   temperature=temperature,
+                                   num_beams=num_beams,
+                                   top_k = top_k,eos_token_id=eos_token_id,
+                                   top_p =top_p,
+                                   num_return_sequences = num_return_sequences,
+                                   do_sample =True, repetition_penalty=repetition_penalty,
+                                  )
+    outputs=outputs[:, inputs["input_ids"].shape[1]:]
+    return tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True), messages
+```
+Usage:
+```
+res,_= generate_response (text_input = "What is collagen?", system_prompt = 'You are a materials scientist.',
+                      num_return_sequences=1,
+                      temperature=1., #the higher the temperature, the more creative the model becomes
+                      max_new_tokens=127,
+                      num_beams=1,
+                      top_k = 50, top_p =0.9, repetition_penalty=1.1,
+                      )
+print (res[0])
+```
+To realize multi-turn interactions, see this example:
+```
+res, messages = generate_response (text_input="What is spider silk?", messages=[])
+messages.append ({"role": "assistant", "content": res[0]}, ) #append result to messages dict
+print (res)
+res, messages = generate_response (text_input="Explain this result in detail.", messages=messages)
+messages.append ({"role": "assistant", "content": res[0]}, ) #append result to messages dict
+print (res)
+res, messages = generate_response (text_input="Provide this in JSON format.", messages=messages)
+messages.append ({"role": "assistant", "content": res[0]}) #append result to messages dict
+print (res)
+```