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--- |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: out |
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results: [] |
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--- |
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This is the Instruction Fine Tuned version of [Tiny Llama](https://github.com/jzhang38/TinyLlama) on [@Teknium1's](https://twitter.com/Teknium1) [openhermes](https://huggingface.co/datasets/teknium/openhermes) dataset. |
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`"The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01."` |
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<details><summary>See axolotl config</summary> |
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axolotl version: `0.3.0` |
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```yaml |
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base_model: ./TinyLlama-1.1B-intermediate-step-1431k-3T |
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model_type: LlamaForCausalLM |
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tokenizer_type: LlamaTokenizer |
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is_llama_derived_model: true |
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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: ./openhermes |
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type: alpaca |
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dataset_prepared_path: |
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val_set_size: 0.05 |
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output_dir: ./out |
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sequence_len: 4096 |
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sample_packing: false |
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adapter: |
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lora_model_dir: |
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lora_r: |
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lora_alpha: |
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lora_dropout: |
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lora_target_linear: |
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lora_fan_in_fan_out: |
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wandb_project: tinyllama-openhermes |
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wandb_entity: tensoic |
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wandb_watch: |
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wandb_name: |
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wandb_log_model: |
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gradient_accumulation_steps: 2 |
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micro_batch_size: 8 |
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num_epochs: 1 |
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optimizer: adamw_bnb_8bit |
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adam_epsilon: 0.00001 |
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max_grad_norm: 1.0 |
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lr_scheduler: cosine |
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learning_rate: 0.0002 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: false |
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fp16: true |
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tf32: false |
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gradient_checkpointing: true |
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early_stopping_patience: |
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resume_from_checkpoint: |
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local_rank: |
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logging_steps: 1 |
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xformers_attention: true |
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flash_attention: |
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warmup_steps: 100 |
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evals_per_epoch: 4 |
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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: zero2.json |
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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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bos_token: "<s>" |
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eos_token: "</s>" |
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unk_token: "<unk>" |
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``` |
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</details><br> |
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The model achieves the following loss: |
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- Loss: 1.3647 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-05 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 1 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 3.0006 | 0.0 | 1 | 1.6838 | |
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| 0.8195 | 0.25 | 451 | 1.4620 | |
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| 0.6836 | 0.5 | 902 | 1.4158 | |
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| 0.6811 | 0.75 | 1353 | 1.3647 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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