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