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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen3-4B |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- code |
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- math |
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--- |
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# Vulpecula-4B |
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> **Vulpecula-4B** is fine-tuned based on the traces of **SK1.1**, consisting of the same 1,000 entries of the **DeepSeek thinking trajectory**, along with fine-tuning on **Fine-Tome 100k** and **Open Math Reasoning** datasets. This specialized 4B parameter model is designed for enhanced mathematical reasoning, logical problem-solving, and structured content generation, optimized for precision and step-by-step explanation. |
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> [!note] |
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> GGUF : [https://huggingface.co/prithivMLmods/Vulpecula-4B-GGUF](https://huggingface.co/prithivMLmods/Vulpecula-4B-GGUF) |
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## Key Features |
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1. **Advanced Mathematical and Logical Reasoning** |
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Fine-tuned on DeepSeek trajectories and Open Math Reasoning to excel at symbolic logic, arithmetic, and complex multi-step math problems, ideal for STEM education and competitions. |
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2. **Trace-Based Fine-Tuning** |
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Leverages SK1.1 trace dataset entries to model deep, interpretable reasoning paths, improving transparency and consistency in problem-solving. |
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3. **Compact Code Understanding** |
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Capable of understanding and generating efficient code snippets in Python, JavaScript, and more, supporting algorithmic explanations and lightweight coding tasks. |
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4. **Factual and Instructional Precision** |
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Trained on curated high-quality data with reasoning benchmarks to minimize hallucinations and strictly follow instructions for structured outputs (Markdown, JSON, tables). |
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5. **Multilingual Capabilities** |
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Supports over 20 languages for technical reasoning and translation, enhancing multilingual educational applications. |
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6. **Optimized Performance for Resource-Constrained Environments** |
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Balances reasoning capability with efficient resource use, suitable for deployment in environments with limited compute. |
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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/Vulpecula-4B" |
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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 = "Solve the equation: 3x + 7 = 22. Show all steps." |
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messages = [ |
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{"role": "system", "content": "You are a step-by-step math tutor."}, |
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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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* Advanced mathematical and logical problem solving |
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* Education-centric STEM tutoring and explanations |
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* Code assistance and debugging for lightweight coding tasks |
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* Structured content generation including JSON, Markdown, and tables |
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* Multilingual reasoning and technical translation |
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* Efficient deployment in low-resource settings with a focus on accuracy and stepwise reasoning |
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## Limitations |
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* Limited creativity in purely open-ended or fictional prompts |
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* May face challenges with ambiguous or multi-intent queries |
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* Smaller context window compared to larger 14B+ models |
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* Possible factual errors in complex edge cases or adversarial inputs |
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## References |
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1. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071) |
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2. Qwen2.5 Technical Report – [https://arxiv.org/pdf/2412.15115](https://arxiv.org/pdf/2412.15115) |
