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.

Judge breakdown for this SFT+DPO checkpoint:

Generalization probes:

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
Copy code
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.