OsamaAliMid
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End of training
Browse files- README.md +59 -180
- adapter_config.json +34 -0
- adapter_model.safetensors +3 -0
- training_args.bin +3 -0
README.md
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---
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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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### Direct Use
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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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#### 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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[More Information Needed]
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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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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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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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base_model: meta-llama/Meta-Llama-3-8B
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library_name: peft
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license: llama3
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tags:
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- trl
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- orpo
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- generated_from_trainer
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model-index:
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- name: ft-Llama3-8b-orpo
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results: []
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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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# ft-Llama3-8b-orpo
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the mlabonne/orpo-dpo-mix-40k dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8983
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- Rewards/chosen: -0.0999
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- Rewards/rejected: -0.1748
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- Rewards/accuracies: 0.4000
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- Rewards/margins: 0.0749
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- Logps/rejected: -1.7478
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- Logps/chosen: -0.9993
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- Logits/rejected: -1.5466
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- Logits/chosen: -1.5315
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- Nll Loss: 0.8281
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- Log Odds Ratio: -0.7026
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- Log Odds Chosen: 0.7314
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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: 8e-06
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_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: linear
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- lr_scheduler_warmup_steps: 10
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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 | Nll Loss | Log Odds Ratio | Log Odds Chosen |
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|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|:--------:|:--------------:|:---------------:|
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| 1.6579 | 0.2 | 25 | 1.2469 | -0.1560 | -0.2318 | 0.5 | 0.0758 | -2.3180 | -1.5595 | -1.2300 | -1.0199 | 1.1776 | -0.6935 | 0.7440 |
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| 1.1014 | 0.4 | 50 | 1.0297 | -0.1262 | -0.1994 | 0.5 | 0.0732 | -1.9942 | -1.2621 | -1.4006 | -1.3743 | 0.9587 | -0.7096 | 0.7137 |
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| 0.9391 | 0.61 | 75 | 0.9463 | -0.1106 | -0.1844 | 0.5 | 0.0738 | -1.8440 | -1.1062 | -1.5970 | -1.5504 | 0.8754 | -0.7083 | 0.7185 |
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| 0.676 | 0.81 | 100 | 0.8983 | -0.0999 | -0.1748 | 0.4000 | 0.0749 | -1.7478 | -0.9993 | -1.5466 | -1.5315 | 0.8281 | -0.7026 | 0.7314 |
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### Framework versions
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- PEFT 0.10.0
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- Transformers 4.39.3
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- Pytorch 2.4.1+cu121
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "meta-llama/Meta-Llama-3-8B",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 32,
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"lora_dropout": 0.05,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 16,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"v_proj",
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"o_proj",
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"gate_proj",
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"q_proj",
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"up_proj",
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"down_proj",
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"k_proj"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"use_rslora": false
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}
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version https://git-lfs.github.com/spec/v1
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oid sha256:af923a6f82d96c531693bffd50f92b03203fb921e22b4b855f45fe50e9d1fdf3
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size 4370592096
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:d3dfb76ebd4d7c653af375468421f3868ae4dfbe7cbbeb820dd3107da237cec7
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size 5240
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