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Model: oro-ai/qwen3-4b-shoppingbench-sft
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- agentic
+- tool-use
+- shopping
+- bittensor
+- shoppingbench
+- qwen3
+---
+
+# Qwen3-4B ShoppingBench SFT
+
+**Paper:** [arXiv:2606.10064](https://arxiv.org/abs/2606.10064)  
+**Code:** [https://github.com/ORO-AI/shoppingbench-trajectory-primitive](https://github.com/ORO-AI/shoppingbench-trajectory-primitive)
+
+Supervised fine-tuning (SFT) of Qwen3-4B on the leak-cluster-guarded ShoppingBench SN15 corpus. This is the base SFT stage of the pipeline.
+
+Companion artifact for the paper *Bittensor Agent Arenas as a Trajectory Primitive:
+Distilling a Shopping Agent from ShoppingBench Subnet Traces*. The published Qwen3-4B
+base scores 18.0% ASR on ShoppingBench; the distilled SFT-family models in this
+collection lift that to 42.7% on a leak-cluster-guarded held-out partition scored
+production-strict.
+
+This is a **merged full model** (Qwen3-4B weights with the trained delta merged in),
+ready to load directly with `transformers` or serve with vLLM. No adapter stacking
+required.
+
+## Training data
+
+- Filtered corpus: [`oro-ai/sn15-shoppingbench-sft-15k`](https://huggingface.co/datasets/oro-ai/sn15-shoppingbench-sft-15k)
+- Raw traces: [`oro-ai/sn15-shoppingbench-traces-18k`](https://huggingface.co/datasets/oro-ai/sn15-shoppingbench-traces-18k)
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+tok = AutoTokenizer.from_pretrained("oro-ai/qwen3-4b-shoppingbench-sft")
+model = AutoModelForCausalLM.from_pretrained("oro-ai/qwen3-4b-shoppingbench-sft", torch_dtype="bfloat16", device_map="auto")
+```
+
+## License
+
+Apache-2.0, inherited from the Qwen3-4B base model.