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--- |
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license: llama3 |
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library_name: peft |
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tags: |
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- axolotl |
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- generated_from_trainer |
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base_model: meta-llama/Meta-Llama-3-8B-Instruct |
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model-index: |
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- name: llama3-8b-instruct-summary |
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results: [] |
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language: |
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- en |
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datasets: |
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- ibivibiv/summary_instruct |
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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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adapter: qlora |
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base_model: meta-llama/Meta-Llama-3-8B-Instruct |
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base_model_config: meta-llama/Meta-Llama-3-8B-Instruct |
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datasets: |
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- path: ibivibiv/summary_instruct |
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type: alpaca |
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flash_attention: true |
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gradient_accumulation_steps: 4 |
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gradient_checkpointing: true |
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hf_use_auth_token: true |
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hub_model_id: ibivibiv/llama3-8b-instruct-summary |
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learning_rate: 0.0002 |
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load_in_4bit: true |
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logging_steps: 1 |
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lora_alpha: 16 |
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lora_dropout: 0.05 |
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lora_r: 32 |
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lora_target_linear: true |
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lr_scheduler: cosine |
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micro_batch_size: 2 |
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model_type: AutoModelForCausalLM |
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num_epochs: 3 |
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optimizer: paged_adamw_32bit |
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output_dir: /job/out |
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sample_packing: true |
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save_safetensors: true |
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sequence_len: 4096 |
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special_tokens: |
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pad_token: <|end_of_text|> |
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tokenizer_type: AutoTokenizer |
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wandb_project: TuneStudio |
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wandb_run_id: summllamma |
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wandb_watch: 'true' |
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warmup_steps: 10 |
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``` |
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</details><br> |
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# llama3-8b-instruct-summary |
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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. |
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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: 0.0002 |
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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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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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: 10 |
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- num_epochs: 3 |
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### Training results |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.40.2 |
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- Pytorch 2.1.2+cu118 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
