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README.md

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