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  # Llama-3.2-3B-ChatGPT-Prompts-Instruct
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  ## Model Description
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  ```python
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  from transformers import AutoTokenizer, AutoModelForCausalLM
 
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  model_name = "sweatSmile/Llama-3.2-3B-ChatGPT-Prompts-Instruct"
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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- model = AutoModelForCausalLM.from_pretrained(model_name)
 
 
 
 
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  # Example usage
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  prompt = "Linux Terminal"
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- inputs = tokenizer(prompt, return_tensors="pt")
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- outputs = model.generate(**inputs, max_length=512, temperature=0.7)
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- response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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  ## Intended Use
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  - The model should not be used to impersonate real individuals or for deceptive purposes
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  - Always disclose when content is AI-generated in professional or public contexts
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- ## Technical Specifications
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-
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- - **Framework:** Transformers, PEFT
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- - **Precision:** FP16
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- - **Memory Optimization:** Gradient checkpointing enabled
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- - **Hardware Requirements:** GPU recommended for inference
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  ## License
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+ ---
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+ language:
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+ - en
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+ library_name: transformers
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+ base_model: meta-llama/Llama-3.2-3B-Instruct
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+ base_model_relation: finetune
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+ tags:
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+ - llama
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+ - chatgpt-prompts
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+ - role-playing
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+ - instruction-tuning
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+ - conversational
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+ - lora
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+ - peft
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+ license: llama3.2
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+ datasets:
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+ - fka/awesome-chatgpt-prompts
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+ pipeline_tag: text-generation
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+ model-index:
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+ - name: Llama-3.2-3B-ChatGPT-Prompts-Instruct
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+ results:
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+ - task:
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+ type: text-generation
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+ name: Text Generation
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+ dataset:
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+ name: awesome-chatgpt-prompts
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+ type: fka/awesome-chatgpt-prompts
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+ metrics:
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+ - name: Training Loss
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+ type: loss
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+ value: 0.28
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+ ---
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+
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  # Llama-3.2-3B-ChatGPT-Prompts-Instruct
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  ## Model Description
 
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  ```python
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  from transformers import AutoTokenizer, AutoModelForCausalLM
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+ import torch
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  model_name = "sweatSmile/Llama-3.2-3B-ChatGPT-Prompts-Instruct"
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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+ model = AutoModelForCausalLM.from_pretrained(
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+ model_name,
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+ torch_dtype=torch.float16,
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+ device_map="auto"
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+ )
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  # Example usage
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  prompt = "Linux Terminal"
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+ messages = [
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+ {"role": "user", "content": prompt}
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+ ]
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+
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+ # Apply chat template
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+ formatted_prompt = tokenizer.apply_chat_template(
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+ messages,
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+ tokenize=False,
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+ add_generation_prompt=True
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+ )
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+
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+ inputs = tokenizer(formatted_prompt, return_tensors="pt")
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+ with torch.no_grad():
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+ outputs = model.generate(
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+ **inputs,
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+ max_new_tokens=512,
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+ temperature=0.7,
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+ do_sample=True,
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+ pad_token_id=tokenizer.eos_token_id
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+ )
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+
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+ response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
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+ print(response)
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  ```
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  ## Intended Use
 
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  - The model should not be used to impersonate real individuals or for deceptive purposes
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  - Always disclose when content is AI-generated in professional or public contexts
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+ ## Framework Versions
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+ - **Transformers:** 4.x
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+ - **PyTorch:** 2.x
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+ - **PEFT:** Latest
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+ - **Datasets:** Latest
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+ - **Tokenizers:** Latest
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  ## License
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