README.md

metadata

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

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

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.