Quick Start
from transformers import AutoModelForImageTextToText
from peft import PeftModel
base_model = AutoModelForImageTextToText.from_pretrained(
"Qwen/Qwen3.5-0.8B",
device_map="auto",
torch_dtype="auto"
)
model = PeftModel.from_pretrained(
base_model,
"adisyonist/waiter-0.8B-lora"
)
'''
The model generates a single JSON object:
```json
{
"agents": ["prepareAddItems", "executeTableAction"],
"answer": "..."
}
For requests that do not require a tool:
{
"agents": [],
"answer": "..."
}
Training Procedure
SFT
The model was fine-tuned using QLoRA with TRL SFTTrainer.
- Base model:
Qwen/Qwen3.5-0.8B
- Method: QLoRA
- Quantization: 4-bit NF4
- Double quantization: Enabled
- Compute dtype: BF16
- LoRA rank: 16
- LoRA alpha: 32
- LoRA dropout: 0.05
- Corpus size: 532,856 examples
- Training split: 426,285 examples
- Validation split: 53,285 examples
- Held-out test split: 53,286 examples
- Languages: 43
Evaluation
Held-out test results:
- Raw agent exact accuracy: 98.81%
- Adjusted agent exact accuracy: 99.40%
- JSON validity: 99.41%
- Answer exact-match: 92.57%
The adjusted routing score accounts for outputs where generation was truncated by the configured token limit while the generated agent chain itself was correct.
Frameworks
- Transformers
- PEFT
- TRL
- PyTorch
- bitsandbytes
License
The adapter is based on
Qwen/Qwen3.5-0.8B
and is released under the Apache License 2.0.