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--- |
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license: apache-2.0 |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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base_model: google/gemma-2b |
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model-index: |
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- name: out1v |
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results: [] |
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pipeline_tag: text-generation |
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--- |
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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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# use google/gemma-7b if you have access |
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base_model: google/gemma-2b |
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model_type: AutoModelForCausalLM |
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tokenizer_type: AutoTokenizer |
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#lora_model_dir: |
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load_in_8bit: false |
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load_in_4bit: true |
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strict: false |
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# huggingface repo |
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datasets: |
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- path: ./python-oasst/cleaned-dataset.jsonl |
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type: oasst |
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val_set_size: 0.20 |
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output_dir: ./out1v |
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adapter: lora |
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lora_r: 8 |
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lora_alpha: 16 |
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lora_dropout: 0.05 |
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lora_target_linear: true |
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gptq: false |
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sequence_len: 4096 |
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sample_packing: false |
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pad_to_sequence_len: true |
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wandb_project: gemma-2b-it |
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wandb_entity: |
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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: 4 |
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micro_batch_size: 4 |
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num_epochs: 2 |
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optimizer: adamw_bnb_8bit |
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lr_scheduler: cosine |
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learning_rate: 0.0002 |
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train_on_inputs: true |
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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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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: |
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flash_attention: true |
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warmup_ratio: 0.1 |
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evals_per_epoch: 4 |
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eval_table_size: |
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eval_max_new_tokens: 128 |
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saves_per_epoch: 1 |
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debug: |
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deepspeed: deepspeed_configs/zero3_bf16.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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``` |
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</details><br> |
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# out1v |
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This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1243 |
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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: 4 |
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- eval_batch_size: 4 |
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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: 32 |
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- total_eval_batch_size: 8 |
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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: 1045 |
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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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| 1.9865 | 0.0 | 1 | 2.2808 | |
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| 1.1033 | 0.25 | 2613 | 1.1338 | |
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| 1.0035 | 0.5 | 5226 | 1.1308 | |
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| 1.0089 | 0.75 | 7839 | 1.1272 | |
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| 0.9816 | 1.0 | 10452 | 1.1238 | |
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| 0.8727 | 1.25 | 13065 | 1.1270 | |
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| 0.9951 | 1.5 | 15678 | 1.1254 | |
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| 1.01 | 1.75 | 18291 | 1.1242 | |
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| 1.0677 | 2.0 | 20904 | 1.1243 | |
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### Framework versions |
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- PEFT 0.10.0 |
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- Transformers 4.40.0.dev0 |
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- Pytorch 2.2.2+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.0 |
