deepoutfit-qwen17b-sft-dpo/README.md

---
license: apache-2.0
base_model: Qwen/Qwen3-1.7B
library_name: transformers
pipeline_tag: text-generation
tags:
  - qwen3
  - text-generation
  - tool-use
  - recommendation
  - fashion
  - sft
  - dpo
language:
  - en
---

# DeepOutfit Qwen1.7B SFT+DPO

This is an experimental full-weight fine-tune of
[`Qwen/Qwen3-1.7B`](https://huggingface.co/Qwen/Qwen3-1.7B) for JSON-action
outfit recommendation traces. It is intended for research on catalog-grounded
fashion/outfit agents that search a product catalog, select five products, and
produce a structured final outfit report.

The model is not a general-purpose shopping assistant by itself. The expected
deployment path is an external tool loop with product-search results and a
validator/scorer around the final JSON report.

## Training

Base model:

- `Qwen/Qwen3-1.7B`

Supervised fine-tuning stage:

- Data: filtered JSON-action outfit rollouts.
- Selection rule used by the local pipeline: score four rollouts per outfit
  query, select the top rollout per query when its score is greater than 60,
  then export selected raw traces for SFT.
- Max length: 16,384.
- Epochs: 3.
- Learning rate: `2e-5`.
- Per-device train batch size: 1.
- Gradient accumulation steps: 16.
- Assistant-only loss: enabled.
- Full fine-tune, not LoRA.

DPO stage:

- Starting checkpoint: the outfit SFT model.
- Data: `100` outfit preference-query training rows and `50` validation rows
  in the local DeepOutfit pipeline.
- Max length: 8,192.
- Epochs: 1.
- Learning rate: `5e-7`.
- DPO beta: `0.1`.
- Per-device train batch size: 1.
- Gradient accumulation steps: 8.
- Full fine-tune, not LoRA.

Uploaded source directory:

`outputs/models/qwen3-1.7b-json-action-outfit-sft-dpo-100q-cont1_20260527_230029`

## Evaluation

Evaluation was run with the local `batch_eval_outfit_models.py` harness on
50 OUTFIT500 queries, one low-temperature rollout per query. Outfit quality was
scored by GPT-4.1 judge. The comparison included Qwen zero-shot, this SFT+DPO
checkpoint, and a later GRPO/RL checkpoint.

| Model | Rows | Overall | Generalization | Entropy | Efficiency | Correctness | Quality | >=70 Quality | Missing Report | Broken Report | Tokens Median | Calls Median | Rollouts/min |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| Qwen3-1.7B zero-shot | 50 | 57.03 | 100 | 0.0416 | 69.38 | 84.8 | 29.93 | 0% | 2% | 22% | 3,632 | 1 | 10.72 |
| DeepOutfit SFT+DPO | 50 | 55.36 | 90 | 0.0650 | 39.71 | 82.0 | 41.58 | 8% | 0% | 30% | 11,240 | 5 | 8.21 |
| DeepOutfit GRPO/RL checkpoint | 50 | 56.69 | 90 | 0.0641 | 40.79 | 88.4 | 39.10 | 6% | 2% | 16% | 11,280 | 5 | 7.05 |

Judge breakdown for this SFT+DPO checkpoint:

| Metric | Value |
|---|---:|
| Judged rows | 50 |
| Judge score > 70 | 4 / 50 |
| Mean judge score | 41.58 |
| Max judge score | 94.27 |
| Min judge score | 21.60 |
| Best average judge submetric | validity gate, 94.0 / 100 |
| Worst average judge submetric | explanation average, 40.8 / 100 |
| Highest failure flag | impractical to wear, 88% |

Generalization probes:

| Probe | Result |
|---|---:|
| Easy math | 8 / 10 |
| JSON formatting | 2 / 2 |
| Factual QA | 2 / 2 |
| Exact string following | 2 / 2 |
| Simple code-output QA | 2 / 2 |

Interpretation: compared with zero-shot Qwen3-1.7B, this checkpoint improves
GPT-4.1 judged outfit quality, but uses more search/tool calls and more tokens.
The dominant remaining failure mode is outfit practicality.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "flavianv/deepoutfit-qwen17b-sft-dpo"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {
        "role": "user",
        "content": "Find a men's backyard BBQ host outfit that is casual, practical, and intentional.",
    }
]
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=1024, temperature=0.2, top_p=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

For the intended JSON-action setting, use the same tool schema and validation
loop as the training/evaluation harness. Standalone generations may reference
products or tool actions that are only meaningful when connected to the product
search tool.

## Limitations

- Experimental research checkpoint, not production validated.
- Optimized for outfit/product-report behavior, not broad assistant quality.
- Can produce incomplete, impractical, or unsupported product combinations.
- Product IDs and search behavior depend on the external catalog/tool harness.
- Easy-math probing shows some drift versus the zero-shot base model.

## License

This checkpoint is released under Apache 2.0, matching the base
`Qwen/Qwen3-1.7B` license metadata.