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Update README.md

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  1. README.md +5 -6
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@@ -3,15 +3,14 @@ library_name: transformers
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  tags: []
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  ---
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- Inference
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  ```
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- model_name='lamm-mit/Bioinspired-Llama-3-1-8B-128k-2-alpha'
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- model_name='lamm-mit/Bioinspired-Llama-3-1-8B-128k-alpha'
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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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- #load_in_8bit=True,
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  device_map="auto",
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  torch_dtype =torch.bfloat16,
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  attn_implementation="flash_attention_2"
@@ -21,7 +20,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
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  ```
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- Function to interact with the model
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  ```
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@@ -66,7 +65,7 @@ def generate_response (text_input="Biology offers amazing",system_prompt='You ar
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  return tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True), messages
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  ```
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-
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  ```
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  res,_= generate_response (text_input = "What is collagen?", system_prompt = 'You are a materials scientist. ',
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  num_return_sequences=1,
 
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  tags: []
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  ---
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+ ## Inference example
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  ```
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+ model_name='lamm-mit/BioinspiredLlama-3-1-8B-128k'
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+
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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",
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  torch_dtype =torch.bfloat16,
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  attn_implementation="flash_attention_2"
 
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  ```
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+ #### Function to interact with the model
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  ```
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  return tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True), messages
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  ```
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+ Usage:
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  ```
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  res,_= generate_response (text_input = "What is collagen?", system_prompt = 'You are a materials scientist. ',
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  num_return_sequences=1,