Update README.md

README.md

@@ -9,4 +9,97 @@ library_name: transformers
 tags:
 - text-generation-inference
 - code
----
+---
+
+![IOP.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/X4wG8maYiZT68QLGW4NPn.png)
+
+# 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 \[Q4\_K\_M] : [https://huggingface.co/prithivMLmods/Draconis-Qwen3\_Math-4B-Preview-Q4\_K\_M-GGUF](https://huggingface.co/prithivMLmods/Draconis-Qwen3_Math-4B-Preview-Q4_K_M-GGUF)
+
+> \[!note]
+> GGUF \[Q5\_K\_M] : [https://huggingface.co/prithivMLmods/Draconis-Qwen3\_Math-4B-Preview-Q5\_K\_M-GGUF](https://huggingface.co/prithivMLmods/Draconis-Qwen3_Math-4B-Preview-Q5_K_M-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)