Model save
Browse files- README.md +63 -0
- all_results.json +9 -0
- generation_config.json +6 -0
- train_results.json +9 -0
- trainer_state.json +1645 -0
README.md
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| 1 |
+
---
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+
library_name: transformers
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license: other
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base_model: apple/OpenELM-1_1B
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tags:
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- trl
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- sft
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- generated_from_trainer
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datasets:
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- generator
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model-index:
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- name: OpenELM-1_1B-SFT
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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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# OpenELM-1_1B-SFT
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This model is a fine-tuned version of [apple/OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) on the generator dataset.
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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: 2e-05
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- train_batch_size: 16
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- eval_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 128
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- total_eval_batch_size: 32
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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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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.3.0
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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all_results.json
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{
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"epoch": 0.9995619798510732,
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"total_flos": 215836199485440.0,
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"train_loss": 1.3199829850460123,
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"train_runtime": 8951.0623,
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"train_samples": 207864,
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"train_samples_per_second": 16.317,
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"train_steps_per_second": 0.127
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.44.2"
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}
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train_results.json
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{
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"epoch": 0.9995619798510732,
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"total_flos": 215836199485440.0,
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"train_loss": 1.3199829850460123,
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"train_runtime": 8951.0623,
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"train_samples": 207864,
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"train_samples_per_second": 16.317,
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"train_steps_per_second": 0.127
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}
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trainer_state.json
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