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Model: flavianv/deepoutfit-qwen17b-sft-dpo
Source: Original Platform

README.md

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+---
+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.