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--- |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- llama-factory |
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- lora |
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- generated_from_trainer |
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base_model: cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser |
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model-index: |
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- name: dolphin-2.6-mistral-7b-dpo-laser-sft-glaive-function-calling-v2-ep1-lora |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# dolphin-2.6-mistral-7b-dpo-laser-sft-glaive-function-calling-v2-ep1-lora |
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This model is a fine-tuned version of [cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser) on the https://huggingface.co/datasets/Yhyu13/glaive-function-calling-v2-llama-factory-convert/blob/main/simple-function-calling-v2_converted_5000_with_function_call_only.json dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0605 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 8 |
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- total_eval_batch_size: 2 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- num_epochs: 1.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.2548 | 0.09 | 100 | 0.1148 | |
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| 0.1149 | 0.18 | 200 | 0.0914 | |
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| 0.0871 | 0.27 | 300 | 0.0831 | |
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| 0.0865 | 0.35 | 400 | 0.0760 | |
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| 0.0802 | 0.44 | 500 | 0.0718 | |
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| 0.0689 | 0.53 | 600 | 0.0702 | |
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| 0.0649 | 0.62 | 700 | 0.0649 | |
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| 0.0637 | 0.71 | 800 | 0.0632 | |
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| 0.0698 | 0.8 | 900 | 0.0619 | |
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| 0.0648 | 0.88 | 1000 | 0.0608 | |
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| 0.0654 | 0.97 | 1100 | 0.0605 | |
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### Framework versions |
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- PEFT 0.7.1 |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
