prithivMLmods/Gamma-Velorum-1.5B-Thinker

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. 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.

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

1. 1.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.

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

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

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

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

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