---
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.