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qwen-fine-tuned
Abhishek0219/qwen-fine-tuned
qwen-fine-tuned is a text generation model from Abhishek0219. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
Qwen/Qwen3-4B-Instruct-2507 fine-tuned on Amazon's Shopping Queries Dataset (ESCI, KDD Cup 2022) to classify query–product relevance as one letter: Exact, Substitute, Complement, Irrelevant. QLoRA (r=16, alpha=32, all…
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At a glance
- Task
- Text Generation
- License
- apache-2.0
- Model type
- qwen3
- Access
- Public
- Created
- Sep 6, 2026
- Updated
- Sep 6, 2026
- SHA
- 61a2dfb0
Try a prompt
Base models
Model card
From the Hugging Face model README
Abhishek0219/qwen-fine-tuned
Qwen/Qwen3-4B-Instruct-2507 fine-tuned on Amazon's Shopping Queries Dataset (ESCI, KDD Cup 2022)
to classify query–product relevance as one letter: Exact, Substitute,
Complement, Irrelevant. QLoRA (r=16, alpha=32, all projection modules),
12k balanced training pairs, 2 epochs; adapter merged into bf16 weights.
| model | ESCI test micro-F1 (5k rows) |
|---|---|
| this model | 0.642 |
| gpt-4o-mini, 0-shot | 0.612 |
| gpt-4o-mini, 4-shot | 0.543 |
Usage (vLLM, OpenAI-compatible)
The model answers with exactly one token, so read the label and its confidence from the first-token logprobs — no decoding needed:
resp = client.chat.completions.create(model="esci", messages=build_messages(query, title, brand, color),
max_tokens=1, temperature=0, logprobs=True, top_logprobs=20)
build_messages and the exact system/user prompt live in prompts.py of the
source repo. Serve with --generation-config vllm (the bundled
generation_config.json is the base model's sampling default).
Data: Shopping Queries Dataset (Amazon Science, Apache-2.0). Adapter:
Abhishek0219/qwen-fine-tuned-lora. Source and deployment YAML: https://github.com/AbhishekG-1plus/esci-distill.
- Task
- Text Generation
- Type
- qwen3
- License
- apache-2.0
- Languages
- en
- Created
- Sep 6, 2026
- Updated
- Sep 6, 2026