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--- |
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base_model: unsloth/Qwen3-4B-Instruct-2507 |
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datasets: |
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- u-10bei/structured_data_with_cot_dataset_512_v2 |
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- u-10bei/structured_data_with_cot_dataset_512_v4 |
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- u-10bei/structured_data_with_cot_dataset_512_v5 |
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language: |
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- en |
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license: apache-2.0 |
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library_name: peft |
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pipeline_tag: text-generation |
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tags: |
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- qlora |
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- lora |
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- structured-output |
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- phase1 |
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--- |
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# Qwen3-4B Structured Output LoRA (Phase 1) |
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This repository provides a **LoRA adapter** fine-tuned from |
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**unsloth/Qwen3-4B-Instruct-2507** using **QLoRA with Unsloth**. |
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It is designed to improve the model’s ability to generate **structured outputs** such as: |
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- JSON |
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- YAML |
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- XML |
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- CSV |
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- other machine-readable formats |
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--- |
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## What This Repository Contains |
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⚠ **Important** |
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This repository contains **LoRA adapter weights only**. |
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It does **not** include the base model. |
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To use this adapter, you must load it on top of the original base model: |
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``` |
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unsloth/Qwen3-4B-Instruct-2507 |
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``` |
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--- |
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## Training Details |
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### Training Phase |
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This adapter was trained as **Phase 1** using the following datasets: |
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- `u-10bei/structured_data_with_cot_dataset_512_v2` |
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- `u-10bei/structured_data_with_cot_dataset_512_v4` |
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- `u-10bei/structured_data_with_cot_dataset_512_v5` |
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Further training (Phase 2) may be performed later using additional datasets. |
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--- |
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### Training Method |
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- Method: **QLoRA (4-bit)** |
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- Framework: **Unsloth + PEFT** |
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- Base model: `unsloth/Qwen3-4B-Instruct-2507` |
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- Maximum sequence length: 1024 |
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- Loss applied only to final assistant output |
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- Intermediate Chain-of-Thought reasoning is masked |
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--- |
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### Hyperparameters (Phase 1) |
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- LoRA rank (r): 64 |
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- LoRA alpha: 128 |
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- Learning rate: 1e-4 |
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- Epochs: 1 |
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- Batch size: 2 |
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- Gradient accumulation: 8 |
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--- |
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## How to Use |
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Example Python code to load and use this adapter: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from peft import PeftModel |
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import torch |
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base_model = "unsloth/Qwen3-4B-Instruct-2507" |
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adapter = "cinnamonrooo/qwen3-structeval-phase1" |
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tokenizer = AutoTokenizer.from_pretrained(base_model) |
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model = AutoModelForCausalLM.from_pretrained( |
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base_model, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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) |
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model = PeftModel.from_pretrained(model, adapter) |
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prompt = "Convert the following text into JSON format:\nName: John\nAge: 25" |
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
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outputs = model.generate(**inputs, max_new_tokens=200) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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--- |
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## License and Terms |
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- Training datasets: MIT License |
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- Base model: subject to original model license |
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- This adapter follows **Apache 2.0 License** |
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Users must comply with both: |
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1. The dataset license |
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2. The original base model terms |
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--- |
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## Notes |
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- This adapter is optimized for **structured generation tasks** |
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- It may not improve general conversational performance |
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- Designed primarily for format-following and machine-readable output accuracy |
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--- |
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### Future Plans |
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- Additional training with more datasets (Phase 2) |
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- Evaluation on structured output benchmarks |
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- Possible quantized release versions |
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--- |
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If you have any questions or feedback, feel free to open an issue. |
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