README.md

---
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.