---
library_name: transformers
tags:
- math
- cot
- text-generation-inference
- preview
- experimental
license: apache-2.0
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
pipeline_tag: text-generation
---

![DMC.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/WYWprTh49LUnIw-HiTcU-.png)

# **Deepmath-Competitive-1.5B-Preview**

> **Deepmath-Competitive-1.5B-Preview** is a **chain-of-thought reasoning model** fine-tuned from **Qwen-1.5B**, purpose-built for solving **mathematical problems** in both **English** and **Chinese** with a focus on **long-context understanding**. It enables advanced reasoning and detailed step-by-step problem solving in a compact form — ideal for competitive exam preparation, tutoring systems, and math-focused AI assistants.

## **Key Features**

1. **Chain-of-Thought Math Reasoning**  
   Specifically trained to output detailed intermediate steps for math problems, Deepmath-Competitive-1.5B-Preview ensures interpretability and logical clarity — vital for learning and validation.

2. **Bilingual Proficiency (English + Chinese)**  
   Proficient in understanding and solving math problems in **both English and Simplified Chinese**, supporting diverse educational needs.

3. **Long-Context Reasoning**  
   Optimized for **long-form math problems** and word problem comprehension, enabling reasoning over extended contexts and compound queries.

4. **Compact yet Powerful**  
   With just 1.5B parameters, it delivers robust performance on arithmetic, algebra, geometry, logic, and competitive exam-style word problems with minimal computational cost.

5. **Structured Step-by-Step Computation**  
   Produces clean, stepwise outputs that mimic expert human problem-solving, helping learners follow the process and logic intuitively.

## **Quickstart with Transformers**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Deepmath-Competitive-1.5B-Preview"

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 Bots**: Delivers in-depth, multi-step solutions for students preparing for competitive and school-level math.
- **Bilingual Educational Apps**: Effective in English and Chinese teaching environments.
- **STEM Reasoning Tools**: Supports structured reasoning across science and engineering questions.
- **Compact LLM Deployments**: Suitable for low-latency environments like mobile apps, edge devices, or web integrations.

## **Limitations**

1. **Domain Focus**:  
   Primarily tuned for mathematics; performance may drop outside STEM or logical domains.

2. **Model Scale**:  
   While efficient, it may underperform on abstract or research-level problems compared to larger models.

3. **Inherited Biases**:  
   As a fine-tune of Qwen-1.5B, some pretraining biases may persist. Review is advised in critical applications.

4. **Prompt Sensitivity**:  
   Performs best with clearly structured prompts and formal question phrasing.