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--- |
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license: apache-2.0 |
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base_model: |
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- prithivMLmods/Qwen3-4B-ft-bf16 |
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datasets: |
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- nvidia/OpenCodeReasoning |
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- efficientscaling/Z1-Code-Reasoning-107K |
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- HuggingFaceH4/CodeAlpaca_20K |
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- mlabonne/FineTome-100k |
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language: |
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- en |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- moe |
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- text-generation-inference |
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- code |
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- math |
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- mot |
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- coder |
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- stem |
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--- |
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# Bootes-Qwen3\_Coder-Reasoning |
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> Bootes-Qwen3\_Coder-Reasoning is a fine-tuned variant of the Qwen3-4B architecture, optimized for high-accuracy code reasoning and structured logical task completion. Trained on the CodeAlpaca\_20K dataset and additional curated programming corpora, this model is designed to perform technical coding, reasoning, and instruction-following tasks with lightweight computational requirements. |
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> [!note] |
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GGUF : https://huggingface.co/prithivMLmods/Bootes-Qwen3_Coder-Reasoning-Q4_K_M-GGUF |
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## Key Features |
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1. Code Reasoning with CodeAlpaca\_20K and More |
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Fine-tuned on CodeAlpaca\_20K and supplementary high-quality datasets focused on: |
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* Multi-language programming tasks |
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* Code explanation, completion, and debugging |
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* Instruction-following with step-wise execution logic |
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2. Cross-Language Code Understanding |
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Handles Python, JavaScript, C++, and more. Ideal for code generation, transformation, bug-fixing, and logic validation. |
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3. Structured Output Generation |
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Delivers responses in Markdown, JSON, YAML, and structured code blocks. Optimized for IDE workflows, documentation tools, and reproducible computation notebooks. |
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4. Instruction-Tuned for Developer Use Cases |
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Maintains strong fidelity to user prompts, especially multi-turn or step-by-step technical instructions across engineering and data workflows. |
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5. Multilingual Reasoning in Technical Domains |
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Capable of technical comprehension and explanation in over 20 human languages, supporting global developer audiences. |
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6. Efficient 4B Architecture |
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Based on Qwen3-4B for a performance-efficient inference model that scales well on mid-range GPUs and cloud deployment setups. |
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## Quickstart with Transformers |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Bootes-Qwen3_Coder-Reasoning" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Write a Python function to check whether a number is a palindrome. Explain each step." |
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messages = [ |
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{"role": "system", "content": "You are a precise coding and reasoning assistant trained on CodeAlpaca and developer datasets."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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``` |
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## Intended Use |
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* Code generation, completion, and explanation |
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* Multi-step algorithmic reasoning |
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* Structured technical document generation (Markdown, JSON, YAML) |
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* Debugging assistance and refactoring suggestions |
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* Technical tutoring and developer assistant workflows |
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* Cross-lingual programming education and translation |
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## Limitations |
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* May underperform on non-code-related creative writing |
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* Limited context window versus larger models |
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* Sensitive to prompt phrasing for ambiguous instructions |
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* Occasionally over-justifies code when brevity is desired |
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## References |
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1. Qwen2.5 Technical Report – [https://arxiv.org/pdf/2412.15115](https://arxiv.org/pdf/2412.15115) |
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2. CodeAlpaca Dataset – [https://github.com/sahil280114/codealpaca](https://github.com/sahil280114/codealpaca) |
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3. YaRN: Context Window Extension for LLMs – [https://arxiv.org/pdf/2309.00071](https://arxiv.org/pdf/2309.00071) |
