初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/TESS-QwenRe-1.5B
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- text-generation-inference
+- code
+- math
+- R1
+license: apache-2.0
+language:
+- en
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+pipeline_tag: text-generation
+---
+
+![4.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/T5H4roOKxLhYhkmPRYrJd.png)
+
+# **TESS-QwenRe-1.5B**
+
+> **TESS-QwenRe-1.5B** is a **chain-of-thought reasoning model**, distilled from **DeepSeek R1 1.5B** and fine-tuned from **Qwen-1.5B**. It is designed to tackle **mathematical problems** in **English** and **Chinese**, with an emphasis on **long-context reasoning** and **step-by-step explanations** — ideal for tutoring, competitive exam preparation, and STEM education tools.
+
+## **Key Features**
+
+1. **Chain-of-Thought Math Reasoning**  
+   Trained to generate intermediate reasoning steps, TESS-QwenRe-1.5B offers transparent and interpretable solutions for math problems — essential for educational clarity and verification.
+
+2. **Bilingual Support (English + Chinese)**  
+   Supports mathematical problem solving and explanation in **both English and Simplified Chinese**, enabling global and bilingual learning applications.
+
+3. **Long-Context Problem Solving**  
+   Specially optimized for solving multi-step, long-form math problems — perfect for word problems, reasoning chains, and competitive math exams.
+
+4. **Distilled from DeepSeek R1 1.5B**  
+   Combines the reasoning capabilities of **DeepSeek R1** with the lightweight and efficient architecture of **Qwen-1.5B**, delivering powerful results in a compact footprint.
+
+5. **Step-by-Step Explanations**  
+   Mimics expert human problem solving with clear, structured steps that help learners follow along and develop understanding.
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/TESS-QwenRe-1.5B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Solve: A train travels 180 km in 3 hours. What is its average speed?"
+messages = [
+    {"role": "system", "content": "You are a helpful tutor skilled in solving math problems with step-by-step explanations."},
+    {"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]
+```
+
+## **Intended Use**
+
+- **Math Tutoring Assistants**: Ideal for school and exam-level math instruction with detailed explanations.
+- **Bilingual EdTech Apps**: Useful in Chinese-English math learning platforms.
+- **STEM Reasoning Tasks**: Reasoning support for science, engineering, and logical problem domains.
+- **Efficient LLM Deployments**: Well-suited for on-device or browser-based reasoning agents.
+
+## **Limitations**
+
+1. **Specialized Domain**:  
+   Tuned for math and logic; may be less effective in open-ended or creative tasks.
+
+2. **Compact Model Constraints**:  
+   As a 1.5B parameter model, it may underperform on extremely complex or abstract problems versus larger models.
+
+3. **Inherited Bias**:  
+   Distilled and fine-tuned from larger models; outputs should be monitored in sensitive contexts.
+
+4. **Prompt Dependency**:  
+   Accurate and structured prompts lead to the best outcomes in problem-solving scenarios.