119 lines
4.1 KiB
Markdown
119 lines
4.1 KiB
Markdown
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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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- trl
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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)
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