104 lines
3.8 KiB
Markdown
104 lines
3.8 KiB
Markdown
---
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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) |