Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- text-generation-inference
+- transformers
+- gguf
+- ollama
+- qwen3
+- python-coding
+base_model: Qwen/Qwen3-4B-Instruct-2507
+pipeline_tag: text-generation
+---
+
+# Truss Python: Qwen3-4B Specialized Coding Assistant
+
+**Truss Python** is a specialized fine-tuned version of **Qwen3-4B-Instruct-2507**, optimized for generating clean, professional, and robust Python code. By focusing on high-quality seed prompts involving standard libraries, asynchronous programming, and data structures, this model prioritizes type safety, documentation, and modern Pythonic patterns.
+
+## 🚀 Model Details
+
+*   **Base Model:** [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
+*   **Architecture:** Qwen3ForCausalLM
+*   **Fine-Tuning Method:** LoRA (Low-Rank Adaptation)
+*   **Quantization:** Q4_K_M (GGUF)
+*   **File Size:** ~2.5 GB
+*   **License:** Apache 2.0 (Inherited from Qwen3)
+
+## ✨ Key Features
+
+*   **Professional Structure:** Consistently generates code with Google-style docstrings and comprehensive type hints.
+*   **Modern Syntax:** Proficient in Python 3.10+ features like `match-case` structural pattern matching.
+*   **Robust Error Handling:** Prioritizes `try/except` blocks and resource management (e.g., `async with`, `contextlib`).
+*   **Standard Library Focus:** Expert-level knowledge of `asyncio`, `threading`, `csv`, `functools`, and `pandas`.
+
+## 💻 Usage with Ollama
+
+This model is optimized for local deployment using **Ollama**.
+
+### 1. Local Import (Using GGUF)
+If you have downloaded the `.gguf` file:
+
+1.  Create a `Modelfile`:
+    ```dockerfile
+    FROM ./truss-qwen3-python-lora.gguf
+    PARAMETER temperature 0.2
+    SYSTEM "You are an expert Python developer. Provide clean, efficient, and well-documented code."
+    ```
+2.  Create the model in your terminal:
+    ```bash
+    ollama create truss-python -f Modelfile
+    ```
+3.  Run the model:
+    ```bash
+    ollama run truss-python
+    ```
+
+## 🧪 Example Prompts
+
+| Task | Prompt |
+| :--- | :--- |
+| **Algorithms** | "Write a binary search function with type hints and no built-in libraries." |
+| **Decorators** | "Create a `@timer` decorator using `functools.wraps` and `time.perf_counter`." |
+| **Async I/O** | "Fetch data from 3 URLs concurrently using `aiohttp` and `asyncio.gather`." |
+| **Data Science** | "Optimize a pandas merge for 1M+ rows using specific dtypes." |
+
+## ⚙️ Technical Specifications
+
+| Parameter | Value |
+| :--- | :--- |
+| **Parameters** | 4 Billion |
+| **Context Length** | 262,144 tokens |
+| **Embedding Dim** | 2,560 |
+| **Head Count** | 32 (Q), 8 (KV) |
+| **RoPE Theta** | 5,000,000 |
+
+## 📜 License
+
+This model is based on Qwen3, which is licensed under the **Apache 2.0 License**. The fine-tuned weights and GGUF conversion are provided for research and development purposes.
+
+---
+
+*Built with ❤️ using Unsloth, llama.cpp, and Ollama.*