Model save
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README.md
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license: gemma
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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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- sft
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- generated_from_trainer
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base_model: google/gemma-2b
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datasets:
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model-index:
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- name: gemma2b-summarize-claude3sonnet-128k
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results: []
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# gemma2b-summarize-claude3sonnet-128k
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This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the
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It achieves the following results on the evaluation set:
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- Loss: 2.
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## Model description
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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:
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 0.8758 | 11.0 | 4422 | 2.5237 |
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| 0.8644 | 12.0 | 4824 | 2.5264 |
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| 0.8641 | 13.0 | 5226 | 2.5268 |
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| 0.8607 | 14.0 | 5628 | 2.5271 |
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| 0.8609 | 15.0 | 6030 | 2.5270 |
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### Framework versions
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- PEFT 0.
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- Transformers 4.
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- Pytorch 2.2.2+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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license: gemma
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library_name: peft
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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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base_model: google/gemma-2b
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datasets:
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- generator
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model-index:
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- name: gemma2b-summarize-claude3sonnet-128k
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results: []
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# gemma2b-summarize-claude3sonnet-128k
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This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the generator dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.6928
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## Model description
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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: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 1.0192 | 1.0 | 402 | 2.4514 |
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| 0.9424 | 2.0 | 804 | 2.4604 |
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| 0.8955 | 3.0 | 1206 | 2.5064 |
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| 0.8659 | 4.0 | 1608 | 2.5306 |
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| 0.8359 | 5.0 | 2010 | 2.5706 |
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| 0.7986 | 6.0 | 2412 | 2.6196 |
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| 0.7778 | 7.0 | 2814 | 2.6583 |
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| 0.7562 | 8.0 | 3216 | 2.6846 |
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| 0.7563 | 9.0 | 3618 | 2.6927 |
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| 0.7461 | 10.0 | 4020 | 2.6928 |
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### Framework versions
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- PEFT 0.11.1
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- Transformers 4.41.2
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- Pytorch 2.2.2+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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adapter_model.safetensors
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all_results.json
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"eval_steps_per_second": 1.995,
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"total_flos": 3.5418612320488653e+18,
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"train_loss": 0.9510796088682083,
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"train_runtime": 20707.2957,
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"train_samples": 126989,
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"train_samples_per_second":
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"train_steps_per_second": 0.
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"train_runtime": 25148.6449,
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"train_samples": 126989,
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"train_samples_per_second": 7.668,
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"train_steps_per_second": 0.16
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runs/Jun10_19-04-55_user-HP-Z8-Fury-G5-Workstation-Desktop-PC/events.out.tfevents.1718013909.user-HP-Z8-Fury-G5-Workstation-Desktop-PC.13878.0
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train_results.json
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"train_samples": 126989,
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"train_samples_per_second": 7.668,
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"train_steps_per_second": 0.16
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}
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trainer_state.json
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