| --- |
| license: llama3 |
| library_name: peft |
| tags: |
| - axolotl |
| - generated_from_trainer |
| base_model: meta-llama/Meta-Llama-3-8B-Instruct |
| model-index: |
| - name: llama3-8b-instruct-summary |
| results: [] |
| language: |
| - en |
| datasets: |
| - ibivibiv/summary_instruct |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| [<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) |
| <details><summary>See axolotl config</summary> |
|
|
| axolotl version: `0.4.0` |
| ```yaml |
| adapter: qlora |
| base_model: meta-llama/Meta-Llama-3-8B-Instruct |
| base_model_config: meta-llama/Meta-Llama-3-8B-Instruct |
| datasets: |
| - path: ibivibiv/summary_instruct |
| type: alpaca |
| flash_attention: true |
| gradient_accumulation_steps: 4 |
| gradient_checkpointing: true |
| hf_use_auth_token: true |
| hub_model_id: ibivibiv/llama3-8b-instruct-summary |
| learning_rate: 0.0002 |
| load_in_4bit: true |
| logging_steps: 1 |
| lora_alpha: 16 |
| lora_dropout: 0.05 |
| lora_r: 32 |
| lora_target_linear: true |
| lr_scheduler: cosine |
| micro_batch_size: 2 |
| model_type: AutoModelForCausalLM |
| num_epochs: 3 |
| optimizer: paged_adamw_32bit |
| output_dir: /job/out |
| sample_packing: true |
| save_safetensors: true |
| sequence_len: 4096 |
| special_tokens: |
| pad_token: <|end_of_text|> |
| tokenizer_type: AutoTokenizer |
| wandb_project: TuneStudio |
| wandb_run_id: summllamma |
| wandb_watch: 'true' |
| warmup_steps: 10 |
| |
| ``` |
|
|
| </details><br> |
|
|
| # llama3-8b-instruct-summary |
|
|
| This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the ibivibiv/summary_instruct dataset. |
| |
| ## Model description |
| |
| More information needed |
| |
| ## Intended uses & limitations |
| |
| More information needed |
| |
| ## Training and evaluation data |
| |
| More information needed |
| |
| ## Training procedure |
| |
| ### Training hyperparameters |
| |
| The following hyperparameters were used during training: |
| - learning_rate: 0.0002 |
| - train_batch_size: 2 |
| - eval_batch_size: 2 |
| - seed: 42 |
| - distributed_type: multi-GPU |
| - num_devices: 2 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 16 |
| - total_eval_batch_size: 4 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 10 |
| - num_epochs: 3 |
|
|
| ### Training results |
|
|
|
|
|
|
| ### Framework versions |
|
|
| - PEFT 0.10.0 |
| - Transformers 4.40.2 |
| - Pytorch 2.1.2+cu118 |
| - Datasets 2.19.1 |
| - Tokenizers 0.19.1 |
