---
library_name: transformers
tags:
- text-generation-inference
- Math
- Code
- Thinker
license: apache-2.0
language:
- en
- zh
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
pipeline_tag: text-generation
---

![Thinker.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/fAOdz1WFMBNJdQM2UNEBe.png)

# **Gamma-Velorum-1.5B-Thinker**

> **Gamma-Velorum-1.5B-Thinker** is a **math and code reasoning model** fine-tuned from **Qwen2.5-1.5B**, crafted to tackle complex **mathematical** and **programming** problems using **chain-of-thought** methodology. It excels in **step-by-step explanations**, long-context understanding, and bilingual support — ideal for education, coding tutors, and logic-intensive applications.

## **Key Features**

1. **Math + Code Chain-of-Thought Reasoning**  
   Trained to provide detailed, structured steps for both **mathematical** and **coding** problems. Gamma-Velorum-1.5B-Thinker explains not just the what, but the *why*, ensuring clarity in logic and computation.

2. **Backed by Qwen2.5-1.5B**  
   Built on the latest Qwen2.5 architecture, bringing improved accuracy, reasoning capabilities, and enhanced tokenizer efficiency.

3. **Long-Context Problem Solving**  
   Capable of handling **long multi-turn questions**, nested logic, and extended code/math scenarios — ideal for competitive exams or coding challenges.

4. **Bilingual (English + Chinese)**  
   Seamlessly understands and reasons through prompts in both **English** and **Simplified Chinese**, making it versatile for global education platforms.

5. **Efficient and Lightweight**  
   With only 1.5B parameters, it strikes a balance between **performance and deployability**, suitable for web, edge, and mobile environments.

## **Quickstart with Transformers**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Gamma-Velorum-1.5B-Thinker"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Write a Python function to calculate factorial of a number."
messages = [
    {"role": "system", "content": "You are a helpful tutor skilled in math and programming. Explain solutions step-by-step."},
    {"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 & Coding Tutors**: Solves word problems, algebra, logic puzzles, and programming challenges with clarity and precision.
- **Bilingual EdTech Apps**: Explains both math and code in English and Chinese for a broader learning reach.
- **STEM Reasoning Engines**: Powers scientific reasoning tools, code-assist bots, and step-by-step logic solvers.
- **Lightweight LLM Use Cases**: Browser-based, embedded systems, or mobile apps for learners and developers.

## **Limitations**

1. **Domain Focused**:  
   Optimized for **STEM and code** tasks — general conversation or abstract creative writing may not be as strong.

2. **Scale Limitations**:  
   As a 1.5B parameter model, it may not match larger models on highly complex logic or long-form generation.

3. **Bias Inheritance**:  
   Carries forward biases from its Qwen2.5 base model — important for sensitive contexts.

4. **Prompt Structuring Matters**:  
   Performs best with explicit, structured prompts for math/code. Ambiguous or casual phrasing may reduce accuracy.
