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--- |
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base_model: slm-research-vn/Qwen2-7B-Instruct-SPPO-Function-call-v2.5 |
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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-v2.6 |
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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-v2.6 |
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This model is a fine-tuned version of [slm-research-vn/Qwen2-7B-Instruct-SPPO-Function-call-v2.5](https://huggingface.co./slm-research-vn/Qwen2-7B-Instruct-SPPO-Function-call-v2.5) 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: 0.3005 |
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- Rewards/chosen: 1.6737 |
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- Rewards/rejected: -0.4932 |
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- Rewards/accuracies: 0.8699 |
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- Rewards/margins: 2.1670 |
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- Logps/rejected: -276.8380 |
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- Logps/chosen: -200.9362 |
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- Logits/rejected: -0.6568 |
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- Logits/chosen: -0.6408 |
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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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| 0.6437 | 0.0916 | 100 | 0.6128 | 0.3050 | 0.0739 | 0.7254 | 0.2311 | -265.4963 | -228.3116 | -0.7319 | -0.7206 | |
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| 0.5175 | 0.1832 | 200 | 0.4987 | 1.1265 | 0.2914 | 0.8237 | 0.8351 | -261.1460 | -211.8815 | -0.7134 | -0.7068 | |
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| 0.3903 | 0.2749 | 300 | 0.4279 | 1.7297 | 0.4889 | 0.8468 | 1.2408 | -257.1960 | -199.8173 | -0.6700 | -0.6642 | |
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| 0.3712 | 0.3665 | 400 | 0.3781 | 1.7272 | 0.2255 | 0.8468 | 1.5017 | -262.4645 | -199.8672 | -0.6756 | -0.6691 | |
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| 0.3064 | 0.4581 | 500 | 0.3477 | 1.7220 | -0.0183 | 0.8613 | 1.7403 | -267.3389 | -199.9704 | -0.6642 | -0.6488 | |
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| 0.3054 | 0.5497 | 600 | 0.3271 | 1.6469 | -0.1977 | 0.8671 | 1.8447 | -270.9281 | -201.4723 | -0.6576 | -0.6407 | |
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| 0.2919 | 0.6413 | 700 | 0.3144 | 1.7376 | -0.3034 | 0.8642 | 2.0410 | -273.0414 | -199.6590 | -0.6753 | -0.6672 | |
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| 0.314 | 0.7329 | 800 | 0.3056 | 1.7037 | -0.4229 | 0.8671 | 2.1266 | -275.4323 | -200.3379 | -0.6685 | -0.6574 | |
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| 0.3014 | 0.8246 | 900 | 0.3020 | 1.6807 | -0.4632 | 0.8699 | 2.1439 | -276.2374 | -200.7971 | -0.6702 | -0.6641 | |
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| 0.268 | 0.9162 | 1000 | 0.2999 | 1.6798 | -0.4929 | 0.8844 | 2.1726 | -276.8312 | -200.8157 | -0.6690 | -0.6635 | |
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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 |
