104 lines
3.6 KiB
Markdown
104 lines
3.6 KiB
Markdown
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- Qwen/Qwen2.5-1.5B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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- math
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- trl
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- SFT
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---
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# **Ross-640-BMath-1.5B**
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> **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.
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> \[!note]
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> GGUF: [https://huggingface.co/prithivMLmods/Ross-640-BMath-1.5B-GGUF](https://huggingface.co/prithivMLmods/Ross-640-BMath-1.5B-GGUF)
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---
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## **Key Features**
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1. **Hard Math Concept Focus**
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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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2. **Step-by-Step Explanations**
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Emphasizes **procedural clarity** over abstract reasoning, offering structured, educational breakdowns of mathematical problems and derivations.
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3. **Symbolic Computation & Annotation**
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Outputs include LaTeX-compatible syntax, inline math symbols, and clear annotation to support academic and technical workflows.
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4. **Educational Utility**
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Optimized for **learning and teaching**, providing clear responses to mathematical queries with minimal noise or conversational drift.
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5. **Lightweight Architecture**
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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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---
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## **Quickstart with Transformers**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Ross-640-BMath-1.5B"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Explain step-by-step how to integrate (x^2 + 1)/(x^3 + 3x) dx."
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messages = [
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{"role": "system", "content": "You are a helpful assistant skilled in solving complex math problems with clear and structured steps."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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---
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## **Intended Use**
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* Detailed mathematical explanations and problem-solving
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* Education-focused tutoring and math derivation tools
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* Math-focused applications and formula documentation
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* Symbolic derivations and LaTeX generation
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* Integration with learning platforms and academic software
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---
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## **Limitations**
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* Not suitable for general-purpose conversation or reasoning tasks
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* Context length constraints may limit effectiveness on large proofs
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* May struggle with non-mathematical or open-ended creative tasks
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* Experimental: Fine-tuned primarily for **explanation clarity**, not deep symbolic reasoning or formal proof validation
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