End of training
Browse files- README.md +117 -191
- adapter_model.bin +1 -1
- adapter_model.safetensors +1 -1
README.md
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---
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base_model: EleutherAI/pythia-160m-deduped
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library_name: peft
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---
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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[More Information Needed]
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### Framework versions
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- PEFT 0.11.1
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base_model: EleutherAI/pythia-160m-deduped
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library_name: peft
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license: apache-2.0
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tags:
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- axolotl
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- generated_from_trainer
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model-index:
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- name: pythia-160m-storytelling
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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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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.4.1`
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```yaml
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base_model: EleutherAI/pythia-160m-deduped
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load_in_8bit: false
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datasets:
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- path: jtatman/storywriting_combined_instruct
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type: alpaca
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dataset_prepared_path: ds-storytelling
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val_set_size: 0.05
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adapter: lora
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lora_model_dir:
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sequence_len: 2048
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lora_r: 16
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lora_alpha: 64
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lora_dropout: 0.05
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lora_target_modules:
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- query_key_value
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lora_target_linear:
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lora_fan_in_fan_out: true # pythia/GPTNeoX lora specific
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wandb_project: pythia
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wandb_entity:
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wandb_watch:
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wandb_name: pythia-160m-storytelling
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wandb_log_model:
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output_dir: ./outputs/lora-alpaca-pythia-160m-storytelling
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gradient_accumulation_steps: 16
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micro_batch_size: 1
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num_epochs: 5
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learning_rate: 0.0006
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lr_scheduler: cosine_with_restarts
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#cosine_min_lr_ratio: 0.1
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train_on_inputs: false
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group_by_length: false
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#bf16: auto
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#fp16: true
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#tf32: false
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float16: true
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flash_attn:
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xformers_attention: true
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optimizer: paged_adamw_8bit
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gpu_memory_limit: 8GiB
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hub_model_id: jtatman/pythia-160m-storytelling
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lora_on_cpu: true
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early_stopping_patience: 3
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#resume_from_checkpoint: outputs/lora-alpaca-pythia-125m/checkpoint-51040
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auto_resume_from_checkpoints: true
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local_rank:
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weight_decay: 0.1
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chat_template: inst
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#evals_per_epoch: 4
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eval_steps: 2000
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logging_steps: 1
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save_steps: 2000
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save_total_limit: 5
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warmup_steps: 1000
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```
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</details><br>
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# pythia-160m-storytelling
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This model is a fine-tuned version of [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 10.3843
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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: 0.0006
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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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- gradient_accumulation_steps: 16
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- total_train_batch_size: 16
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine_with_restarts
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- lr_scheduler_warmup_steps: 1000
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- num_epochs: 5
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 5.4891 | 0.0012 | 1 | 4.5640 |
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| 8.4799 | 2.4467 | 2000 | 9.1436 |
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| 9.9198 | 4.8944 | 4000 | 10.3843 |
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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 2.3.0+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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size 1183112
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