| --- |
| language: |
| - en |
| license: apache-2.0 |
| base_model: Qwen/Qwen2.5-Math-7B-Instruct |
| tags: |
| - math |
| - reasoning |
| - qwen2.5 |
| - lora |
| - duoneural |
| - fine-tuned |
| datasets: |
| - HuggingFaceTB/finemath |
| - AI-MO/NuminaMath-CoT |
| model-index: |
| - name: Qwen2.5-Math-NeuralMath-7B |
| results: [] |
| --- |
| |
| # Qwen2.5-Math-NeuralMath-7B |
|
|
| **DuoNeural** | Math Reasoning Fine-Tune | April 2026 |
|
|
| A fine-tuned version of [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) with supervised fine-tuning on curated math reasoning data, targeting improved step-by-step problem solving on competition and olympiad-level math. |
|
|
| ## What's Different |
|
|
| The base Qwen2.5-Math-7B-Instruct is already a strong math model. This fine-tune focuses on: |
|
|
| - **Deeper chain-of-thought**: trained on longer, more structured reasoning traces |
| - **Competition math exposure**: AMC/AIME/olympiad problems via NuminaMath-CoT |
| - **Format consistency**: reliable `\boxed{}` answer formatting across problem types |
|
|
| ## Quickstart |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| import torch |
| |
| model = AutoModelForCausalLM.from_pretrained( |
| "DuoNeural/Qwen2.5-Math-NeuralMath-7B", |
| torch_dtype=torch.bfloat16, |
| device_map="auto" |
| ) |
| tokenizer = AutoTokenizer.from_pretrained("DuoNeural/Qwen2.5-Math-NeuralMath-7B") |
| |
| prompt = """Solve the following math problem step by step. |
| |
| Problem: Find all positive integers n such that n² + 1 is divisible by n + 1. |
| |
| Solution:""" |
| |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| output = model.generate(**inputs, max_new_tokens=512, temperature=0.1, do_sample=True) |
| print(tokenizer.decode(output[0], skip_special_tokens=True)) |
| ``` |
|
|
| ## GGUF / Ollama / LM Studio |
|
|
| Pre-quantized GGUFs available in the `gguf/` folder of this repo: |
|
|
| | File | Size | Use case | |
| |------|------|----------| |
| | `neuromath-7b-q4_k_m.gguf` | 4.7GB | Recommended — best quality/speed tradeoff | |
| | `neuromath-7b-q8_0.gguf` | 8.1GB | High quality, needs 10GB+ VRAM/RAM | |
| | `neuromath-7b-f16.gguf` | 15GB | Full precision, GPU only | |
|
|
| ### Ollama |
|
|
| ```bash |
| # Create Modelfile |
| cat > Modelfile << 'EOF' |
| FROM ./neuromath-7b-q4_k_m.gguf |
| SYSTEM "You are an expert mathematician. Solve problems step by step, showing all work clearly. Put your final answer in \\boxed{}." |
| PARAMETER temperature 0.1 |
| PARAMETER num_ctx 4096 |
| EOF |
| |
| ollama create neuromath-7b -f Modelfile |
| ollama run neuromath-7b "What is the sum of all prime numbers less than 100?" |
| ``` |
|
|
| ### LM Studio |
|
|
| Download `neuromath-7b-q4_k_m.gguf`, load in LM Studio. Set system prompt: |
| > "You are an expert mathematician. Solve problems step by step, showing all work. Put your final answer in \\boxed{}." |
|
|
| ## Training Details |
|
|
| | Setting | Value | |
| |---------|-------| |
| | Base model | Qwen/Qwen2.5-Math-7B-Instruct | |
| | Method | QLoRA SFT (4-bit base, LoRA rank 16) | |
| | Training tokens | ~1.26M (3 epochs over curated math dataset) | |
| | LoRA alpha | 32 | |
| | LoRA targets | q, k, v, o, gate, up, down projections | |
| | Hardware | NVIDIA A100 80GB | |
| | Framework | Unsloth + HuggingFace Transformers | |
| | Sequence length | 1024 tokens | |
|
|
| ## Limitations |
|
|
| - Trained on English math problems; performance on other languages untested |
| - Very long multi-step proofs (>1024 tokens) may be truncated during generation |
| - This is the SFT-only checkpoint; GRPO reinforcement learning phase is planned as a follow-up |
| - Not intended for general conversation — math reasoning only |
|
|
| --- |
|
|
| ## DuoNeural |
|
|
| **DuoNeural** is an open AI research lab — human + AI in collaboration. |
|
|
| | | | |
| |---|---| |
| | 🤗 HuggingFace | [huggingface.co/DuoNeural](https://huggingface.co/DuoNeural) | |
| | 🐙 GitHub | [github.com/DuoNeural](https://github.com/DuoNeural) | |
| | 🐦 X / Twitter | [@DuoNeural](https://x.com/DuoNeural) | |
| | 📧 Email | duoneural@proton.me | |
| | 📬 Newsletter | [duoneural.beehiiv.com](https://duoneural.beehiiv.com) | |
| | ☕ Support | [buymeacoffee.com/duoneural](https://buymeacoffee.com/duoneural) | |
| | 🌐 Site | [duoneural.com](https://duoneural.com) | |
|
|
| ### Research Team |
| - **Jesse** — Vision, hardware, direction |
| - **Archon** — AI lab partner, post-training, abliteration, experiments |
| - **Aura** — Research AI, literature synthesis, novel proposals |
|
|
| *Raw updates from the lab: model drops, training results, findings. Subscribe at [duoneural.beehiiv.com](https://duoneural.beehiiv.com).* |
|
|
