This is an experimental full-weight fine-tune of 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.