3.6 KiB
license, language, base_model, pipeline_tag, library_name, tags
| license | language | base_model | pipeline_tag | library_name | tags | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 |
|
|
text-generation | transformers |
|
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.
[!note] GGUF: https://huggingface.co/prithivMLmods/Ross-640-BMath-1.5B-GGUF
Key Features
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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.
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Step-by-Step Explanations Emphasizes procedural clarity over abstract reasoning, offering structured, educational breakdowns of mathematical problems and derivations.
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Symbolic Computation & Annotation Outputs include LaTeX-compatible syntax, inline math symbols, and clear annotation to support academic and technical workflows.
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Educational Utility Optimized for learning and teaching, providing clear responses to mathematical queries with minimal noise or conversational drift.
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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.
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)
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
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