初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/Capella-Qwen3-DS-V3.1-4B
Source: Original Platform

README.md

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+---
+license: apache-2.0
+tags:
+- trl
+- text-generation-inference
+- math
+- science
+- code
+- v3.1
+- stem
+language:
+- en
+base_model:
+- Qwen/Qwen3-4B
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+![12.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/bRrbrQsIP7JOye9EW7lfK.png)
+
+# **Capella-Qwen3-DS-V3.1-4B**
+
+> **Capella-Qwen3-DS-V3.1-4B** is a reasoning-focused model fine-tuned on **Qwen-4B** using **DeepSeek v3.1 synthetic traces (10K)**.
+> It specializes in **random event simulations**, **logical problem analysis**, and structured reasoning tasks.
+> The model blends symbolic precision, probabilistic logic, and structured output fluency—making it an ideal tool for researchers, educators, and developers working with uncertainty modeling and event-driven analysis.
+
+> \[!note]
+> GGUF: [https://huggingface.co/prithivMLmods/Capella-Qwen3-DS-V3.1-4B-GGUF](https://huggingface.co/prithivMLmods/Capella-Qwen3-DS-V3.1-4B-GGUF)
+
+---
+
+## **Key Features**
+
+1. **Event Simulation & Logical Analysis**
+   Fine-tuned on **10,000 synthetic traces** from DeepSeek v3.1 to model random events, probability-driven reasoning, and logical decision-making.
+
+2. **Advanced Code Reasoning & Generation**
+   Supports multi-language coding with explanations, optimization hints, and error detection—ideal for algorithm synthesis, stochastic simulations, and debugging.
+
+3. **Mathematical & Probabilistic Problem Solving**
+   Performs analytical reasoning across probability, statistics, and mathematics—explaining concepts, solving equations, and simulating uncertain outcomes.
+
+4. **Hybrid Symbolic-Probabilistic Thinking**
+   Combines structured logic, probabilistic inference, and chain-of-thought reasoning, delivering robust performance on uncertainty-driven tasks.
+
+5. **Structured Output Mastery**
+   Seamlessly generates output in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, suited for technical documentation, simulations, and structured analysis.
+
+6. **Optimized Lightweight Footprint for Versatile Deployment**
+   Balances performance and efficiency, making it deployable on **mid-range GPUs**, **offline clusters**, and **edge AI systems**.
+
+---
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Capella-Qwen3-DS-V3.1-4B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Simulate the probability of rolling two dice and getting a sum greater than 9. Show the reasoning."
+
+messages = [
+    {"role": "system", "content": "You are a reasoning tutor skilled in probability, logic, and coding."},
+    {"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**
+
+* Random event simulation, probability modeling, and uncertainty analysis
+* Logical problem-solving in research and education
+* Structured data and technical content generation
+* STEM-focused chatbot or API for probabilistic reasoning tools
+* Deployment in mid-resource environments requiring efficient reasoning
+
+---
+
+## **Limitations**
+
+* Not tuned for general-purpose or creative writing
+* Context limitations may hinder multi-document or full codebase analysis
+* Specialized for simulations and logical reasoning—general chat may underperform
+* Prioritizes probabilistic and logical precision over casual or emotional tone