初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/Draconis-Qwen3_Math-4B-Preview
Source: Original Platform

README.md

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+---
+license: apache-2.0
+datasets:
+- unsloth/OpenMathReasoning-mini
+- mlabonne/FineTome-100k
+language:
+- en
+base_model:
+- Qwen/Qwen3-4B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- moe
+- math
+- code
+- text-generation-inference
+- trl
+---
+![Draconis.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/CpdC-5a9DZO7NMY6DcW9M.png)
+
+# Draconis-Qwen3\_Math-4B-Preview
+
+> **Draconis-Qwen3\_Math-4B-Preview** is fine-tuned on the **Qwen3-4B** architecture, optimized for excellence in **mathematical reasoning**, **logical problem solving**, and **structured content generation**. This preview model focuses on precision, step-by-step reasoning, and efficient inference, making it ideal for educational and technical applications where reliability and compact performance are essential.
+
+> [!note]
+GGUF [Q4_K_M] : 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
+
+## Key Features
+
+1. **Mathematical and Logical Reasoning**
+   Finetuned to solve symbolic logic, arithmetic, and multi-step mathematical problems, making it ideal for STEM learning, competitions, and educational use.
+
+2. **Compact Code Understanding**
+   Efficient in writing and interpreting code in Python, JavaScript, and other languages, suitable for lightweight coding tasks and algorithmic explanations.
+
+3. **Factual Precision**
+   Trained on high-quality, curated data with reasoning benchmarks to reduce hallucinations and ensure correctness in technical outputs.
+
+4. **Instruction-Tuned**
+   Strong adherence to instructions, ideal for structured queries, step-by-step problem solving, and producing formatted outputs (Markdown, JSON, tables).
+
+5. **Multilingual Support**
+   Capable of understanding and responding in over 20 languages, useful for multilingual education and technical translation.
+
+6. **Efficient Performance**
+   Based on the 4B parameter variant of Qwen3, optimized for resource-constrained environments without compromising core reasoning capability.
+
+## Quickstart with Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Draconis-Qwen3_Math-4B-Preview"
+
+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
+
+* Solving math and logic problems
+* Code assistance and basic debugging
+* Education-focused applications (STEM tutoring)
+* Structured content generation (e.g., JSON, Markdown)
+* Multilingual reasoning and translations
+* Lightweight deployment in reasoning tasks
+
+## Limitations
+
+* Limited creativity in open-ended or fictional content
+* May struggle with ambiguous or multi-intent prompts
+* Smaller context window compared to 14B+ variants
+* Still subject to factual errors in edge cases or adversarial queries
+
+## References
+
+1. [AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models] :  https://arxiv.org/pdf/2504.16891
+2. [YaRN: Efficient Context Window Extension of Large Language Models] : https://arxiv.org/pdf/2309.00071