Ross-640-BMath-1.5B

Ross-640-BMath-1.5B is an experimental, high-precision math explanation model fine-tuned on Qwen2-1.5B, designed to provide step-by-step mathematical derivations and detailed concept explanations across a wide range of mathematical domains. It is not optimized for general reasoning or conversation, and focuses primarily on structured, non-reasoning math workflows including algebra, calculus, number theory, and combinatorics.

GGUF: https://huggingface.co/prithivMLmods/Ross-640-BMath-1.5B-GGUF

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Key Features

1. Hard Math Concept Focus Specializes in algebra, calculus, combinatorics, linear algebra, number theory, and more—delivering fine-tuned, low-latency outputs ideal for math-intensive applications.

2. Step-by-Step Explanations Emphasizes procedural clarity over abstract reasoning, offering structured, educational breakdowns of mathematical problems and derivations.

3. Symbolic Computation & Annotation Outputs include LaTeX-compatible syntax, inline math symbols, and clear annotation to support academic and technical workflows.

4. Educational Utility Optimized for learning and teaching, providing clear responses to mathematical queries with minimal noise or conversational drift.

5. Lightweight Architecture Built on Qwen2-1.5B and fine-tuned for efficiency and precision, making it suitable for deployment in resource-constrained environments, educational tools, or math-centric chat interfaces.

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Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Ross-640-BMath-1.5B"

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

prompt = "Explain step-by-step how to integrate (x^2 + 1)/(x^3 + 3x) dx."

messages = [
    {"role": "system", "content": "You are a helpful assistant skilled in solving complex math problems with clear and structured steps."},
    {"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]
print(response)

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Intended Use

• Detailed mathematical explanations and problem-solving
• Education-focused tutoring and math derivation tools
• Math-focused applications and formula documentation
• Symbolic derivations and LaTeX generation
• Integration with learning platforms and academic software

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Limitations

• Not suitable for general-purpose conversation or reasoning tasks
• Context length constraints may limit effectiveness on large proofs
• May struggle with non-mathematical or open-ended creative tasks
• Experimental: Fine-tuned primarily for explanation clarity, not deep symbolic reasoning or formal proof validation