End of training
Browse files- README.md +14 -14
- config.json +32 -32
- model-00001-of-00003.safetensors +1 -1
- model-00002-of-00003.safetensors +1 -1
- model-00003-of-00003.safetensors +1 -1
- sparsification_sftt.py +1 -1
README.md
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@@ -4,18 +4,18 @@ base_model: mistralai/Mistral-7B-v0.1
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tags:
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- generated_from_trainer
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model-index:
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- name: Mistral_Sparse_refined_web_graceful_reg_90p_2024-03-
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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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# Mistral_Sparse_refined_web_graceful_reg_90p_2024-03-
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss:
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## Model description
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- eval_batch_size: 1
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- seed: 0
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- distributed_type: multi-GPU
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- num_devices:
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- gradient_accumulation_steps: 8
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- total_train_batch_size:
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- total_eval_batch_size:
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- training_steps: 200
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 3.
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### Framework versions
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tags:
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- generated_from_trainer
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model-index:
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+
- name: Mistral_Sparse_refined_web_graceful_reg_90p_2024-03-14
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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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+
# Mistral_Sparse_refined_web_graceful_reg_90p_2024-03-14
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 3.2117
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## Model description
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- eval_batch_size: 1
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- seed: 0
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 32
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- total_eval_batch_size: 4
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- training_steps: 200
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 3.6973 | 0.01 | 25 | 2.3992 |
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| 3.6504 | 0.02 | 50 | 2.3855 |
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| 3.6737 | 0.02 | 75 | 2.3872 |
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| 3.5868 | 0.03 | 100 | 2.4532 |
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| 3.5604 | 0.04 | 125 | 2.4999 |
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| 3.4312 | 0.05 | 150 | 2.5201 |
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| 3.3355 | 0.06 | 175 | 2.5216 |
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| 3.3825 | 0.06 | 200 | 2.5236 |
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### Framework versions
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config.json
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"thresholds": [
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],
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"thresholds": [
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0.0631895586848259,
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0.07923770695924759,
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0.089267797768116,
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0.10732196271419525,
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0.12738214433193207,
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0.1414242684841156,
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0.1735205501317978,
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0.17552657425403595,
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0.1775325983762741,
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0.18756268918514252,
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0.1935807317495346,
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0.19759276509284973,
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0.21364091336727142,
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0.22367100417613983,
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0.23169508576393127,
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0.22367100417613983,
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0.22968906164169312,
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0.23169508576393127,
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0.23971915245056152,
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0.2457372099161148,
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0.2577733099460602,
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0.2678034007549286,
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0.27382147312164307,
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0.27582746744155884,
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0.277833491563797
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],
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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model-00001-of-00003.safetensors
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model-00002-of-00003.safetensors
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model-00003-of-00003.safetensors
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sparsification_sftt.py
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@@ -585,7 +585,7 @@ class GracefulRegularizationScheduler(TrainerCallback):
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enable_sparse_silu(base_model)
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self.trainer.evaluate()
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save_act_hist(base_model, self.act_hist_path)
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set_sparse_threshold(base_model, self.targeted_sparsity,
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deactivate_stats(base_model)
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self.trainer.use_sparse_regularization = self.keep_regularization_with_kill
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# set_layer_specific_regularization(model.get_base_model())
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enable_sparse_silu(base_model)
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self.trainer.evaluate()
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save_act_hist(base_model, self.act_hist_path)
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set_sparse_threshold(base_model, self.targeted_sparsity, False)
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deactivate_stats(base_model)
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self.trainer.use_sparse_regularization = self.keep_regularization_with_kill
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# set_layer_specific_regularization(model.get_base_model())
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