Instructions to use MF0619/SFTv16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MF0619/SFTv16 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "MF0619/SFTv16") - Notebooks
- Google Colab
- Kaggle
Upload 8 files
Browse files- README.md +3 -3
- adapter_model.safetensors +1 -1
README.md
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---
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base_model: Qwen/Qwen3-4B-Instruct-2507
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datasets:
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- u-10bei/
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language:
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license: apache-2.0
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- Base model: Qwen/Qwen3-4B-Instruct-2507
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- Method: QLoRA (4-bit)
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- Max sequence length:
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- Epochs: 1
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- Learning rate: 2e-05
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- LoRA: r=64, alpha=128
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## Sources & Terms (IMPORTANT)
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Training data: u-10bei/
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Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License.
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Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.
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---
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base_model: Qwen/Qwen3-4B-Instruct-2507
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datasets:
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- u-10bei/structured_data_with_cot_dataset_v2
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language:
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- en
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license: apache-2.0
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- Base model: Qwen/Qwen3-4B-Instruct-2507
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- Method: QLoRA (4-bit)
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- Max sequence length: 2048
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- Epochs: 1
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- Learning rate: 2e-05
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- LoRA: r=64, alpha=128
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## Sources & Terms (IMPORTANT)
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Training data: u-10bei/structured_data_with_cot_dataset_v2
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Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License.
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Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.
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adapter_model.safetensors
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