143 lines
3.2 KiB
Markdown
143 lines
3.2 KiB
Markdown
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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-WildChat
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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/WildChat_ShareGPT
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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-3-WildChat
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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-WildChat
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wandb_log_model:
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hub_model_id: SynDa/Llama-3-8B-WildChat
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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-WildChat
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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: 0.8197
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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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| 1.1455 | 0.0003 | 1 | 1.3389 |
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| 0.9084 | 0.3333 | 1128 | 0.8551 |
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| 0.9265 | 0.6667 | 2256 | 0.8363 |
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| 0.9086 | 1.0 | 3384 | 0.8210 |
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| 0.8257 | 1.3164 | 4512 | 0.8214 |
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| 0.8306 | 1.6497 | 5640 | 0.8197 |
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| 0.8252 | 1.9831 | 6768 | 0.8197 |
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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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