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license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-4B
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- code
- math
---
# Vulpecula-4B
> **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.
> [!note]
> GGUF : [https://huggingface.co/prithivMLmods/Vulpecula-4B-GGUF](https://huggingface.co/prithivMLmods/Vulpecula-4B-GGUF)
## Key Features
1. **Advanced Mathematical and Logical Reasoning**
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.
2. **Trace-Based Fine-Tuning**
Leverages SK1.1 trace dataset entries to model deep, interpretable reasoning paths, improving transparency and consistency in problem-solving.
3. **Compact Code Understanding**
Capable of understanding and generating efficient code snippets in Python, JavaScript, and more, supporting algorithmic explanations and lightweight coding tasks.
4. **Factual and Instructional Precision**
Trained on curated high-quality data with reasoning benchmarks to minimize hallucinations and strictly follow instructions for structured outputs (Markdown, JSON, tables).
5. **Multilingual Capabilities**
Supports over 20 languages for technical reasoning and translation, enhancing multilingual educational applications.
6. **Optimized Performance for Resource-Constrained Environments**
Balances reasoning capability with efficient resource use, suitable for deployment in environments with limited compute.
## Quickstart with Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Vulpecula-4B"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Solve the equation: 3x + 7 = 22. Show all steps."
messages = [
{"role": "system", "content": "You are a step-by-step math tutor."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```
## Intended Use
* Advanced mathematical and logical problem solving
* Education-centric STEM tutoring and explanations
* Code assistance and debugging for lightweight coding tasks
* Structured content generation including JSON, Markdown, and tables
* Multilingual reasoning and technical translation
* Efficient deployment in low-resource settings with a focus on accuracy and stepwise reasoning
## Limitations
* Limited creativity in purely open-ended or fictional prompts
* May face challenges with ambiguous or multi-intent queries
* Smaller context window compared to larger 14B+ models
* Possible factual errors in complex edge cases or adversarial inputs
## References
1. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)
2. Qwen2.5 Technical Report – [https://arxiv.org/pdf/2412.15115](https://arxiv.org/pdf/2412.15115) |