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Model: tensorhydra/qwen3-8b-aimo3-tir Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: other
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license_name: qwen-research
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base_model: Qwen/Qwen2.5-8B
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tags:
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- qwen
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- lora
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- merged
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- math
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- reasoning
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- tool-integrated-reasoning
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- aimo
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- safetensors
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datasets:
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- jeannkouagou/aimo3-tool-integrated-reasoning
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Qwen3-8B AIMO3 Tool-Integrated Reasoning
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## Model Summary
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A LoRA fine-tuned version of Qwen-8B trained for **tool-integrated reasoning** on the AIMO3 competition dataset (generated by GPT-OSS-120B). The LoRA adapters have been **merged** into the base model and saved in SafeTensors format for straightforward deployment.
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| Property | Details |
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|---|---|
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| Base Model | Qwen-8B |
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| Fine-tuning Method | LoRA (merged) |
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| Format | SafeTensors (BF16) |
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| Parameters | ~8B |
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| Disk Size | ~16GB |
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| Max Context | 8192 tokens |
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---
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## Model Details
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### LoRA Configuration
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| Hyperparameter | Value |
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|---|---|
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| Rank (r) | 16 |
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| Alpha | 32 |
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| Dropout | 0.05 |
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| Bias | none |
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| Target Modules | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |
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### Training Hyperparameters
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| Hyperparameter | Value |
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|---|---|
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| Precision | BFloat16 (no quantization) |
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| Epochs | 2 |
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| Steps | 8750 (~1 epoch) |
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| Per-device Batch Size | 2 |
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| Gradient Accumulation Steps | 8 (effective batch: 16) |
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| Learning Rate | 2e-4 |
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| LR Scheduler | Cosine with warmup |
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| Warmup Ratio | 0.03 |
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| Weight Decay | 0.01 |
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| Max Gradient Norm | 1.0 |
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| Max Sequence Length | 8192 |
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| Optimizer | AdamW (Fused) |
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### Hardware & Infrastructure
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- **Platform**: Kaggle
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- **GPU**: Single NVIDIA H100 (80GB)
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- **Attention**: Flash Attention 2
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- **Optimizations**: Gradient checkpointing, TF32, fused optimizer
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---
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## Training Data
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- **Dataset**: [AIMO3 Tool-Integrated Reasoning Dataset](https://www.kaggle.com/datasets/jeannkouagou/aimo3-tool-integrated-reasoning) (synthesized by GPT-OSS-120B)
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- **Split**: 97.5% train / 2.5% validation
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- **Format**: CSV with problem–solution pairs
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**Supported column names:**
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- Input: `problem`, `question`, `input`, `prompt`
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- Output: `solution`, `answer`, `output`, `response`, `completion`
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### Instruction Format
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Training uses a ChatML-style format:
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```
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<|im_start|>user
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{prompt}<|im_end|>
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<|im_start|>assistant
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{response}<|im_end|>
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```
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### Training Loss
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The model is trained for 8750 steps (~ 1 epoch) before stopping. Below are the train and validation loss curves for the entire training session.
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---
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## Usage
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### Load the Model
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Since the LoRA adapters are already merged, PEFT is **not required**:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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"tensorhydra/qwen-8b-aimo3-reasoning",
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trust_remote_code=True,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(
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"tensorhydra/qwen-8b-aimo3-reasoning",
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trust_remote_code=True
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)
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```
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### Inference
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```python
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prompt = "Solve this problem: What is 2 + 2?"
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formatted_prompt = f"user\n{prompt}\nassistant\n"
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inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.9,
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do_sample=True
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=False)
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print(response)
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```
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### Batch Inference
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```python
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prompts = [
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"Solve: 15 + 27 = ?",
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"What is the derivative of x^2?",
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"Calculate the area of a circle with radius 5"
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]
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formatted_prompts = [
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f"user\n{p}\nassistant\n"
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for p in prompts
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]
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inputs = tokenizer(formatted_prompts, return_tensors="pt", padding=True).to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
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for response in tokenizer.batch_decode(outputs, skip_special_tokens=False):
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print(response)
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print("-" * 80)
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```
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### Quantized Inference (Lower VRAM)
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```python
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# 8-bit (~8GB VRAM)
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model = AutoModelForCausalLM.from_pretrained(
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"tensorhydra/qwen-8b-aimo3-reasoning",
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load_in_8bit=True,
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device_map="auto"
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)
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# 4-bit (~4GB VRAM)
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model = AutoModelForCausalLM.from_pretrained(
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"tensorhydra/qwen-8b-aimo3-reasoning",
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load_in_4bit=True,
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device_map="auto"
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)
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```
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---
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## Memory Requirements
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| Mode | VRAM |
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|---|---|
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| BF16 (full) | ~16GB |
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| 8-bit quantized | ~8GB |
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| 4-bit quantized | ~4GB |
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---
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## Repository Structure
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```
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model/
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├── config.json
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├── generation_config.json
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├── model.safetensors.index.json
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├── model-00001-of-0000X.safetensors
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├── ...
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├── tokenizer_config.json
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├── tokenizer.json
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└── special_tokens_map.json
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```
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---
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## Intended Use
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- Mathematical reasoning and problem solving
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- Tool-integrated step-by-step reasoning
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- Educational and research applications
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- Production deployment (merged model, no PEFT dependency)
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## Limitations
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- Fine-tuned on a narrow reasoning domain; may not generalize well to other tasks
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- Hard context limit of 8192 tokens
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- Performance is bounded by the quality and distribution of the synthetic training data
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- Full merged model requires ~16GB storage (vs. ~100–200MB for LoRA adapters alone)
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---
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## Links
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- **Dataset**: [jeannkouagou/aimo3-tool-integrated-reasoning](https://www.kaggle.com/datasets/jeannkouagou/aimo3-tool-integrated-reasoning)
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- **Fine-tuning Notebook**: [tensorhydra/qwen3-8b-aimo3-finetune](https://www.kaggle.com/code/tensorhydra/qwen3-8b-aimo3-finetune)
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---
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## Citation
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```bibtex
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@misc{qwen-lora-aimo3,
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title = {Qwen-8B LoRA Fine-tuned for Tool-Integrated Reasoning},
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author = {tensorhydra},
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year = {2025},
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howpublished = {Kaggle Model Hub},
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note = {Merged LoRA model in SafeTensors format}
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}
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```
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---
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## Acknowledgements
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- **Base model**: [Qwen-8B](https://huggingface.co/Qwen) by Alibaba Cloud
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- **Training frameworks**: Hugging Face Transformers & PEFT
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- **Dataset synthesis**: GPT-OSS-120B
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- **Serialization**: SafeTensors
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- **Training platform**: Kaggle (H100 GPU)
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## License
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This model inherits the license of the base Qwen-8B model. Please refer to the [Qwen license terms](https://huggingface.co/Qwen/Qwen2.5-8B/blob/main/LICENSE) before use.
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