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--- |
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license: llama3 |
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base_model: meta-llama/Meta-Llama-3-8B |
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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: Llama-3-8B-ShareGPT |
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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: `0.4.0` |
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```yaml |
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base_model: meta-llama/Meta-Llama-3-8B |
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model_type: LlamaForCausalLM |
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tokenizer_type: AutoTokenizer |
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load_in_8bit: false |
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load_in_4bit: false |
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strict: false |
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datasets: |
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- path: flydust/ShareGPT-Vicuna-unfiltered |
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type: sharegpt |
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conversation: llama3 |
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dataset_prepared_path: last_run_prepared |
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val_set_size: 0.001 |
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output_dir: ./out_Llama-8B-sharegpt-vicuna |
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sequence_len: 8192 |
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sample_packing: true |
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eval_sample_packing: false |
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pad_to_sequence_len: true |
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wandb_project: SynDa |
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wandb_entity: |
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wandb_watch: |
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wandb_name: Llama-3-8B-Sharegpt-vicuna |
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wandb_log_model: |
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hub_model_id: SynDa/Llama-3-8B-ShareGPT |
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gradient_accumulation_steps: 8 |
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micro_batch_size: 1 |
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num_epochs: 2 |
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optimizer: paged_adamw_8bit |
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lr_scheduler: cosine |
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learning_rate: 2e-5 |
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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: |
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tf32: false |
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gradient_checkpointing: true |
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gradient_checkpointing_kwargs: |
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use_reentrant: false |
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early_stopping_patience: |
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resume_from_checkpoint: |
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logging_steps: 1 |
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xformers_attention: |
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flash_attention: true |
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warmup_steps: 100 |
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evals_per_epoch: 3 |
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eval_table_size: |
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saves_per_epoch: 1 |
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debug: |
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deepspeed: |
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weight_decay: 0.0 |
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fsdp: |
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fsdp_config: |
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special_tokens: |
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pad_token: <|end_of_text|> |
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``` |
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</details><br> |
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# Llama-3-8B-ShareGPT |
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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 None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4747 |
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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: 1 |
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- eval_batch_size: 1 |
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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: 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: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 0.7768 | 0.0012 | 1 | 0.8449 | |
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| 0.6441 | 0.3331 | 288 | 0.5582 | |
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| 0.5294 | 0.6662 | 576 | 0.5212 | |
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| 0.5777 | 0.9993 | 864 | 0.4849 | |
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| 0.4499 | 1.3218 | 1152 | 0.4766 | |
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| 0.4507 | 1.6549 | 1440 | 0.4752 | |
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| 0.4856 | 1.9880 | 1728 | 0.4747 | |
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### Framework versions |
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- Transformers 4.40.2 |
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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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