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README.md

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+---
+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 AI research lab focused on post-training techniques, efficient architectures, and edge deployment. We document our wins, losses, and learnings publicly.
+
+- GitHub: [DuoNeural](https://github.com/DuoNeural)
+- HuggingFace: [DuoNeural](https://huggingface.co/DuoNeural)