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--- |
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license: llama3.1 |
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language: |
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- en |
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pipeline_tag: question-answering |
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--- |
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# Deepthought-8B |
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Deepthought-8B is a small and capable reasoning model built on LLaMA-3.1 8B, designed to make AI reasoning more transparent and controllable. Despite its relatively small size, it achieves sophisticated reasoning capabilities that rival much larger models. |
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## Model Description |
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Deepthought-8B is designed with a unique approach to problem-solving, breaking down its thinking into clear, distinct, documented steps. The model outputs its reasoning process in a structured JSON format, making it easier to understand and validate its decision-making process. |
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### Key Features |
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- **Transparent Reasoning**: Step-by-step documentation of the thought process |
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- **Programmable Approach**: Customizable reasoning patterns without model retraining |
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- **Test-time Compute Scaling**: Flexible reasoning depth based on task complexity |
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- **Efficient Scale**: Runs on 16GB+ VRAM |
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- **Structured Output**: JSON-formatted reasoning chains for easy integration |
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Try out Deepthought-8B on our Ruliad interface: https://chat.ruliad.co |
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## Technical Requirements |
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- Python 3.6+ |
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- PyTorch |
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- Transformers library |
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- 16GB+ VRAM |
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- Optional: Flash Attention 2 for improved performance |
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## Installation |
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```bash |
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pip install torch transformers |
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# Optional: Install Flash Attention 2 for better performance |
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pip install flash-attn |
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``` |
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## Usage |
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1. First, set your HuggingFace token as an environment variable: |
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```bash |
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export HF_TOKEN=your_token_here |
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export HF_HUB_ENABLE_HF_TRANSFER=1 |
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``` |
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2. Use the model in your Python code: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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# Initialize the model |
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model_name = "ruliad/Deepthought-8b-llama-v0.01-alpha" |
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tokenizer = AutoTokenizer.from_pretrained( |
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model_name, |
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add_bos_token=False, |
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trust_remote_code=True, |
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padding="left", |
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torch_dtype=torch.bfloat16, |
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) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype=torch.bfloat16, |
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device_map="auto", |
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attn_implementation="flash_attention_2", # Use "default" if flash_attn not installed |
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use_cache=True, |
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trust_remote_code=True, |
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) |
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``` |
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3. Run the provided example script: |
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```bash |
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python deepthought_inference.py |
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``` |
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## Example Output |
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The model provides structured reasoning in JSON format: |
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```json |
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{ |
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"step": 1, |
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"type": "problem_understanding", |
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"thought": "Understanding the user's objective for the task." |
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} |
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``` |
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Each reasoning chain includes multiple steps: |
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1. Problem understanding |
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2. Data gathering |
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3. Analysis |
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4. Calculation (when applicable) |
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5. Verification |
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6. Conclusion drawing |
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7. Implementation |
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## Performance |
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Deepthought-8B demonstrates strong performance across various benchmarks: |
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- Step-by-step problem-solving |
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- Coding and mathematical tasks |
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- Instruction following with transparent reasoning |
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- Scalable performance with test-time compute |
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## Limitations |
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Current known limitations include: |
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- Complex mathematical reasoning |
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- Long-context processing |
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- Edge case handling |
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## License |
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The model is available under a commercial license for enterprise use. |
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## Citation |
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If you use this model in your research, please cite: |
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```bibtex |
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@misc{Deepthought2024, |
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author = {Ruliad AI}, |
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title = {Deepthought-8B: A Small and Capable Reasoning Model}, |
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year = {2024}, |
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publisher = {Ruliad} |
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} |
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``` |
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## Support |
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For questions and feedback: |
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- Twitter: @ruliad_ai |
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- Email: team@ruliad.co |
