初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/Regulus-Qwen3-R1-Llama-Distill-1.7B
Source: Original Platform

README.md

@@ -0,0 +1,107 @@
+---
+datasets:
+- Magpie-Align/Magpie-Reasoning-V2-250K-CoT-Deepseek-R1-Llama-70B
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3-1.7B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- trl
+- text-generation-inference
+- llama
+- distill
+- experimental
+---
+
+![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/6IACMTfvjkw6sQI7swljn.png)
+
+# **Regulus-Qwen3-R1-Llama-Distill-1.7B**
+
+> **Regulus-Qwen3-R1-Llama-Distill-1.7B** is a **distilled reasoning model** fine-tuned on **Qwen/Qwen3-1.7B** using **Magpie-Align/Magpie-Reasoning-V2-250K-CoT-DeepSeek-R1-Llama-70B**.
+> The training leverages **distilled traces from DeepSeek-R1-Llama-70B**, transferring advanced reasoning patterns into a lightweight 1.7B parameter model.
+> It is specialized for **chain-of-thought reasoning across code, math, and science**, optimized for efficiency and mid-resource deployment.
+
+> \[!note]
+> GGUF: [https://huggingface.co/prithivMLmods/Regulus-Qwen3-R1-Llama-Distill-1.7B-GGUF](https://huggingface.co/prithivMLmods/Regulus-Qwen3-R1-Llama-Distill-1.7B-GGUF)
+
+---
+
+## **Key Features**
+
+1. **Distilled Reasoning from Large-Scale Models**
+   Trained with **distilled traces from DeepSeek-R1-Llama-70B**, preserving structured **chain-of-thought reasoning** in a smaller, faster model.
+
+2. **Unified Code + Math + Science Reasoning**
+   Strong performance across computational logic, programming tasks, and scientific problem solving.
+
+3. **Structured Chain-of-Thought Generation**
+   Produces clear, step-by-step explanations for algorithms, equations, and symbolic tasks.
+
+4. **Optimized Lightweight Footprint**
+   Maintains reasoning depth while being deployable on **mid-range GPUs**, **offline clusters**, and **edge AI systems**.
+
+5. **Multi-Format Output Support**
+   Generates responses in **LaTeX**, **Markdown**, **JSON**, and **tabular formats** for technical and research workflows.
+
+---
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Regulus-Qwen3-R1-Llama-Distill-1.7B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Explain step by step how to solve a system of linear equations using Gaussian elimination."
+
+messages = [
+    {"role": "system", "content": "You are a reasoning assistant skilled in math, code, and scientific logic."},
+    {"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**
+
+* **Math and algorithm tutoring** with clear reasoning steps
+* **Code reasoning and synthesis** for debugging and algorithm design
+* **Scientific problem solving** in physics, chemistry, and biology
+* **Structured educational assistant** for step-by-step learning
+* **Efficient deployment** where distilled reasoning fidelity is required
+
+## **Limitations**
+
+* Derived from **distilled traces** – reasoning may simplify compared to full-scale teacher models
+* Not tuned for general-purpose conversation or creative writing
+* Context length limits multi-document or long-codebase reasoning
+* Optimized for structured reasoning, not emotional or casual dialogue