base_model, library_name, license, pipeline_tag, tags
| base_model | library_name | license | pipeline_tag | tags | |||||
|---|---|---|---|---|---|---|---|---|---|
| Qwen/Qwen2.5-7B-Instruct | transformers | apache-2.0 | text-generation |
|
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
- 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
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)