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---
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
---
# **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. |