Instructions to use ravindraog/sentinel-coder-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ravindraog/sentinel-coder-qlora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "ravindraog/sentinel-coder-qlora") - Notebooks
- Google Colab
- Kaggle
Model save
Browse files- README.md +5 -5
- adapter_model.safetensors +1 -1
- final/adapter_config.json +3 -3
- final/adapter_model.safetensors +1 -1
- final/training_args.bin +1 -1
README.md
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This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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### Framework versions
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This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1132
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## Model description
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 0.4273 | 0.7030 | 50 | 0.4052 |
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| 0.1574 | 1.4060 | 100 | 0.2152 |
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| 0.1124 | 2.1090 | 150 | 0.1263 |
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| 0.0864 | 2.8120 | 200 | 0.1132 |
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### Framework versions
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adapter_model.safetensors
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final/adapter_config.json
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"q_proj",
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"k_proj",
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"down_proj",
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"up_proj",
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"
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"v_proj"
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"task_type": "CAUSAL_LM",
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"down_proj",
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"gate_proj",
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"o_proj",
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"up_proj",
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"k_proj",
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"q_proj",
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"v_proj"
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"task_type": "CAUSAL_LM",
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final/adapter_model.safetensors
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final/training_args.bin
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