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--- |
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base_model: slm-research-vn/Qwen2-7B-Instruct-SPPO-Function-call-v2.8 |
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datasets: |
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- slm-research-vn/dpo-format-function-calling-v4 |
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- slm-research-vn/dpo-format-glaive-code-assistant-v3-with-mistral-large-slm-iter4 |
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- argilla/dpo-mix-7k |
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library_name: peft |
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tags: |
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- alignment-handbook |
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- trl |
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- dpo |
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- generated_from_trainer |
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model-index: |
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- name: Qwen2-7B-Instruct-SPPO-Function-call-v2.12 |
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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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# Qwen2-7B-Instruct-SPPO-Function-call-v2.12 |
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This model is a fine-tuned version of [slm-research-vn/Qwen2-7B-Instruct-SPPO-Function-call-v2.8](https://huggingface.co/slm-research-vn/Qwen2-7B-Instruct-SPPO-Function-call-v2.8) on the slm-research-vn/dpo-format-function-calling-v4, the slm-research-vn/dpo-format-glaive-code-assistant-v3-with-mistral-large-slm-iter4 and the argilla/dpo-mix-7k datasets. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3322 |
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- Rewards/chosen: 0.5523 |
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- Rewards/rejected: -0.7005 |
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- Rewards/accuracies: 0.9017 |
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- Rewards/margins: 1.2528 |
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- Logps/rejected: -278.7327 |
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- Logps/chosen: -129.0717 |
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- Logits/rejected: -0.5984 |
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- Logits/chosen: -0.7738 |
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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: 1e-06 |
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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: 8 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 8 |
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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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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | |
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|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| |
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| 0.6806 | 0.0916 | 100 | 0.6816 | 0.0303 | 0.0099 | 0.6445 | 0.0205 | -264.5260 | -139.5110 | -0.5879 | -0.7638 | |
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| 0.5704 | 0.1832 | 200 | 0.5993 | 0.3495 | 0.1473 | 0.8237 | 0.2023 | -261.7780 | -133.1277 | -0.5881 | -0.7638 | |
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| 0.5032 | 0.2749 | 300 | 0.5313 | 0.5795 | 0.1792 | 0.8526 | 0.4003 | -261.1383 | -128.5271 | -0.5893 | -0.7651 | |
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| 0.4548 | 0.3665 | 400 | 0.4727 | 0.6406 | 0.0523 | 0.8844 | 0.5884 | -263.6780 | -127.3051 | -0.5901 | -0.7660 | |
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| 0.3823 | 0.4581 | 500 | 0.4235 | 0.6412 | -0.1314 | 0.8931 | 0.7726 | -267.3507 | -127.2934 | -0.5914 | -0.7672 | |
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| 0.3513 | 0.5497 | 600 | 0.3843 | 0.6087 | -0.3415 | 0.9133 | 0.9502 | -271.5532 | -127.9448 | -0.5936 | -0.7693 | |
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| 0.3444 | 0.6413 | 700 | 0.3571 | 0.5871 | -0.5028 | 0.9104 | 1.0898 | -274.7784 | -128.3763 | -0.5965 | -0.7721 | |
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| 0.3486 | 0.7329 | 800 | 0.3427 | 0.5681 | -0.6155 | 0.9104 | 1.1836 | -277.0341 | -128.7559 | -0.5971 | -0.7725 | |
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| 0.3317 | 0.8246 | 900 | 0.3349 | 0.5586 | -0.6739 | 0.9133 | 1.2326 | -278.2013 | -128.9451 | -0.5993 | -0.7748 | |
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| 0.3077 | 0.9162 | 1000 | 0.3328 | 0.5530 | -0.6974 | 0.9075 | 1.2504 | -278.6715 | -129.0585 | -0.5998 | -0.7754 | |
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### Framework versions |
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- PEFT 0.12.0 |
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- Transformers 4.44.0 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
