--- language: - en library_name: transformers base_model: meta-llama/Llama-3.2-3B-Instruct base_model_relation: finetune tags: - llama - chatgpt-prompts - role-playing - instruction-tuning - conversational - lora - peft license: llama3.2 datasets: - fka/awesome-chatgpt-prompts pipeline_tag: text-generation model-index: - name: Llama-3.2-3B-ChatGPT-Prompts-Instruct results: - task: type: text-generation name: Text Generation dataset: name: awesome-chatgpt-prompts type: fka/awesome-chatgpt-prompts metrics: - name: Training Loss type: loss value: 0.28 --- # Llama-3.2-3B-ChatGPT-Prompts-Instruct ## Model Description This is a fine-tuned version of Meta's Llama-3.2-3B-Instruct model, specifically trained on the awesome-chatgpt-prompts dataset to excel at role-playing and prompt-based interactions. The model has been optimized to understand and respond to various professional and creative roles with enhanced accuracy and context awareness. ## Model Details - **Base Model:** meta-llama/Llama-3.2-3B-Instruct - **Model Type:** Causal Language Model - **Fine-tuning Method:** LoRA (Low-Rank Adaptation) - **Training Dataset:** fka/awesome-chatgpt-prompts - **Model Size:** 3B parameters - **Quantization:** 4-bit (BitsAndBytesConfig) ## Training Details ### Training Configuration - **LoRA Rank:** 4 - **LoRA Alpha:** 8 - **Learning Rate:** 3e-4 - **Batch Size:** 8 - **Epochs:** 10 - **Max Sequence Length:** 64 - **Gradient Accumulation Steps:** 3 - **Optimizer:** AdamW with cosine learning rate scheduler - **Weight Decay:** 0.01 - **Warmup Ratio:** 0.05 ### Training Results - **Final Training Loss:** 0.28 - **Training Steps:** 190 - **Training Runtime:** 399.47 seconds - **Convergence:** Stable convergence with proper gradient norms ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_name = "sweatSmile/Llama-3.2-3B-ChatGPT-Prompts-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.float16, device_map="auto" ) # Example usage prompt = "Linux Terminal" messages = [ {"role": "user", "content": prompt} ] # Apply chat template formatted_prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = tokenizer(formatted_prompt, return_tensors="pt") with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.7, do_sample=True, pad_token_id=tokenizer.eos_token_id ) response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True) print(response) ``` ## Intended Use This model is designed for: - **Role-playing scenarios:** Acting as various professionals (developers, translators, terminals, etc.) - **Educational purposes:** Learning different professional contexts and responses - **Creative writing assistance:** Generating contextually appropriate responses for different roles - **Prompt engineering research:** Understanding how models respond to role-based instructions ## Capabilities The model excels at: - Understanding and adopting various professional roles - Generating contextually appropriate responses - Maintaining consistency within assigned roles - Following complex instructions with role-specific knowledge - Adapting communication style based on the requested persona ## Example Interactions **Input:** "English Translator and Improver" **Output:** The model will adopt the role of a professional translator and language improver, offering translation services and language enhancement capabilities. **Input:** "Linux Terminal" **Output:** The model will simulate a Linux terminal environment, responding to commands as a real terminal would. ## Limitations - Model responses are generated based on training data and may not always reflect real-world accuracy - Performance may vary depending on the complexity and specificity of role-based requests - The model should not be used for generating harmful, biased, or inappropriate content - Outputs should be verified for factual accuracy, especially in professional contexts ## Ethical Considerations - This model should be used responsibly and ethically - Users should be aware that this is an AI model and not substitute for real professional expertise - The model should not be used to impersonate real individuals or for deceptive purposes - Always disclose when content is AI-generated in professional or public contexts ## Framework Versions - **Transformers:** 4.x - **PyTorch:** 2.x - **PEFT:** Latest - **Datasets:** Latest - **Tokenizers:** Latest ## License This model inherits the license from the base Llama-3.2-3B-Instruct model. Please refer to Meta's license terms for usage restrictions and requirements. ## Citation ```bibtex @model{llama32-chatgpt-prompts-instruct, title={Llama-3.2-3B-ChatGPT-Prompts-Instruct}, author={sweatSmile}, year={2025}, base_model={meta-llama/Llama-3.2-3B-Instruct}, dataset={fka/awesome-chatgpt-prompts} } ``` ## Contact For questions, issues, or feedback regarding this model, please create an issue in the model repository or contact the model author.