Instructions to use hebertgo/knowledgebase-intent-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use hebertgo/knowledgebase-intent-llm with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir knowledgebase-intent-llm hebertgo/knowledgebase-intent-llm
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
Upload fine-tuned intent detection model - 2025-06-24 12:11:05
Browse files- README.md +1 -1
- model.safetensors +1 -1
- model_config.json +1 -1
README.md
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## Training Details
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- **Fine-tuning Method**: LoRA with model fusion
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- **Export Date**: 2025-06-24T12:
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- **Fusion Completed**: True
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## Expected Outputs
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## Training Details
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- **Fine-tuning Method**: LoRA with model fusion
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- **Export Date**: 2025-06-24T12:11:01.720513
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- **Fusion Completed**: True
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## Expected Outputs
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 868628547
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version https://git-lfs.github.com/spec/v1
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oid sha256:8b85c86a9a4b65af819370dc438d6e3119ff9eae2e319000950d68ebbca2c174
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size 868628547
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model_config.json
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"examples": 5000,
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"iterations": 100,
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"training_completed": true,
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"export_timestamp": "2025-06-24T12:
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"fusion_completed": true
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},
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"ios_integration": {
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"examples": 5000,
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"iterations": 100,
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"training_completed": true,
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"export_timestamp": "2025-06-24T12:11:01.720513",
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"fusion_completed": true
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},
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"ios_integration": {
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