File size: 4,343 Bytes
d94657c bd0cc83 d94657c bd0cc83 d94657c bd0cc83 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 | ---
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).*
|