---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- agent
- sft
- lora
- sql
- household-tasks
---

# Agent Model - Matsuo LLM Advanced Competition (Phase IMDB1)

Fine-tuned model for DB operation (SQL) and household navigation tasks.

## Model Details

- **Base Model**: [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
- **Training Method**: Supervised Fine-Tuning (SFT) with QLoRA + Instruction Masking
- **LoRA Configuration**: r=32, alpha=64
- **Training Data**: 6,750 samples (DB operation + household task trajectories)

## Training Details

### Data Composition
- **DB operation data**: SQL query generation samples including Spider/BIRD public datasets and Qwen2.5-72B-Instruct distilled samples
- **Household task data**: Synthetic agent trajectories for navigation and manipulation tasks
- **Total**: 6,750 samples

### Training Configuration
- **Epochs**: 1.0
- **Batch Size**: 4 (effective: 16 with gradient accumulation)
- **Learning Rate**: 5e-6 (cosine schedule, 5% warmup)
- **Max Sequence Length**: 4096
- **Quantization**: 4-bit QLoRA during training, merged to bf16 for inference
- **Special**: Instruction Masking (loss computed only on assistant response tokens)

## Quick Start

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "astom-M/matsuo-llm-advanced-phase-imdb1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

messages = [
    {"role": "user", "content": "Your task here..."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.1)
response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print(response)
```

## Framework Versions

- Transformers: 4.57.6
- PyTorch: 2.10.0
- PEFT: 0.11.0
- TRL: 0.24.0
- Unsloth: 2025.5.8

## License

Apache 2.0 (same as base model Qwen2.5-7B-Instruct)

## Model Development

This model was trained for the Matsuo Lab LLM Advanced Competition 2025:
- Approved base model: Qwen2.5-7B-Instruct
- Training method: SFT with QLoRA (r=32, alpha=64) + Instruction Masking
- Data: Synthetic DB operation + household task trajectories (no ALFWorld or AgentBench data used)
- Distillation source: Qwen2.5-72B-Instruct (whitelist model)