README.md

---
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).*

### DuoNeural Research Publications

| Title | DOI |
|-------|-----|
| [Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning](https://doi.org/10.5281/zenodo.19775622) | [10.5281/zenodo.19775622](https://doi.org/10.5281/zenodo.19775622) |
| [Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments](https://doi.org/10.5281/zenodo.19810620) | [10.5281/zenodo.19810620](https://doi.org/10.5281/zenodo.19810620) |
| [Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?](https://doi.org/10.5281/zenodo.19846804) | [10.5281/zenodo.19846804](https://doi.org/10.5281/zenodo.19846804) |

*Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.*