| --- |
| license: llama3 |
| library_name: peft |
| tags: |
| - axolotl |
| - generated_from_trainer |
| base_model: meta-llama/Meta-Llama-3-8B |
| model-index: |
| - name: query-gen |
| results: [] |
| --- |
| |
| <!-- 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 |
| base_model: meta-llama/Meta-Llama-3-8B |
| model_type: LlamaForCausalLM |
| tokenizer_type: AutoTokenizer |
| |
| load_in_8bit: false |
| load_in_4bit: true |
| strict: false |
| |
| hub_model_id: davanstrien/query-gen |
| |
| datasets: |
| - path: davanstrien/query-gen |
| type: alpaca |
| dataset_prepared_path: last_run_prepared |
| val_set_size: 0.05 |
| output_dir: ./lora-out |
| |
| sequence_len: 1024 |
| sample_packing: true |
| pad_to_sequence_len: true |
| |
| adapter: lora |
| lora_model_dir: |
| lora_r: 32 |
| lora_alpha: 16 |
| lora_dropout: 0.05 |
| lora_target_linear: true |
| lora_fan_in_fan_out: |
| |
| wandb_project: axolotl |
| wandb_entity: |
| wandb_watch: |
| wandb_name: query |
| wandb_log_model: |
| |
| gradient_accumulation_steps: 4 |
| micro_batch_size: 10 |
| num_epochs: 4 |
| optimizer: adamw_bnb_8bit |
| lr_scheduler: cosine |
| learning_rate: 0.0002 |
| |
| train_on_inputs: false |
| group_by_length: false |
| bf16: auto |
| fp16: |
| tf32: false |
| |
| gradient_checkpointing: true |
| early_stopping_patience: |
| resume_from_checkpoint: |
| local_rank: |
| logging_steps: 1 |
| xformers_attention: |
| flash_attention: true |
| s2_attention: |
| |
| warmup_steps: 10 |
| evals_per_epoch: 4 |
| eval_table_size: |
| eval_max_new_tokens: 128 |
| saves_per_epoch: 1 |
| debug: |
| deepspeed: |
| weight_decay: 0.0 |
| fsdp: |
| fsdp_config: |
| special_tokens: |
| pad_token: <|end_of_text|> |
| |
| ``` |
|
|
| </details><br> |
|
|
| # query-gen |
|
|
| 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. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.2679 |
|
|
| ## 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: 10 |
| - eval_batch_size: 10 |
| - seed: 42 |
| - distributed_type: multi-GPU |
| - num_devices: 4 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 160 |
| - total_eval_batch_size: 40 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 10 |
| - num_epochs: 4 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:------:|:----:|:---------------:| |
| | 2.8337 | 0.0071 | 1 | 2.8390 | |
| | 1.414 | 0.2540 | 36 | 1.4018 | |
| | 1.3212 | 0.5079 | 72 | 1.3332 | |
| | 1.304 | 0.7619 | 108 | 1.3042 | |
| | 1.2874 | 1.0159 | 144 | 1.2900 | |
| | 1.229 | 1.2522 | 180 | 1.2835 | |
| | 1.2247 | 1.5062 | 216 | 1.2779 | |
| | 1.2362 | 1.7601 | 252 | 1.2708 | |
| | 1.2364 | 2.0141 | 288 | 1.2663 | |
| | 1.1734 | 2.2504 | 324 | 1.2691 | |
| | 1.1781 | 2.5044 | 360 | 1.2683 | |
| | 1.1995 | 2.7584 | 396 | 1.2658 | |
| | 1.1861 | 3.0123 | 432 | 1.2626 | |
| | 1.1332 | 3.2487 | 468 | 1.2680 | |
| | 1.1438 | 3.5026 | 504 | 1.2680 | |
| | 1.1553 | 3.7566 | 540 | 1.2679 | |
| |
| |
| ### Framework versions |
| |
| - PEFT 0.10.0 |
| - Transformers 4.40.2 |
| - Pytorch 2.1.2+cu118 |
| - Datasets 2.19.1 |
| - Tokenizers 0.19.1 |
