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--- |
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base_model: EleutherAI/pythia-160m-deduped |
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library_name: peft |
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license: apache-2.0 |
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tags: |
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- axolotl |
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- relora |
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- generated_from_trainer |
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model-index: |
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- name: pythia-160m-storytelling |
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results: [] |
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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: `0.4.1` |
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```yaml |
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base_model: EleutherAI/pythia-160m-deduped |
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load_in_8bit: |
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datasets: |
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- path: jtatman/storywriting_combined_instruct |
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type: alpaca |
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dataset_prepared_path: ds-storytelling |
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chat_template: inst |
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val_set_size: 0.01 |
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adapter: lora |
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lora_model_dir: |
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sequence_len: 2048 |
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lora_r: 16 |
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lora_alpha: 32 |
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lora_dropout: 0.05 |
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lora_target_modules: |
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- query_key_value |
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lora_target_linear: true |
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lora_fan_in_fan_out: true # pythia/GPTNeoX lora specific |
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lora_modules_to_save: |
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- embed_in |
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- embed_out |
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- lm_head |
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lora_on_cpu: false |
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# ReLoRA configuration |
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# # Must use either 'lora' or 'qlora' adapter, and does not support fsdp or deepspeed |
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# relora_steps: # Number of steps per ReLoRA restart |
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# relora_warmup_steps: # Number of per-restart warmup steps |
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# relora_anneal_steps: # Number of anneal steps for each relora cycle |
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# relora_prune_ratio: # threshold for optimizer magnitude when pruning |
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# relora_cpu_offload: # True to perform lora weight merges on cpu during restarts, for modest gpu memory savings |
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relora_steps: 200 |
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relora_warmup_steps: 10 |
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relora_cpu_offload: false |
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wandb_project: pythia |
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wandb_entity: |
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wandb_watch: |
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wandb_name: pythia-160m-storytelling |
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wandb_log_model: |
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output_dir: ./outputs/lora-alpaca-pythia-160m-storytelling |
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gradient_accumulation_steps: 16 |
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micro_batch_size: 1 |
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num_epochs: 3 |
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learning_rate: 0.0006 |
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lr_scheduler: cosine_with_restarts |
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#cosine_min_lr_ratio: 0.1 |
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train_on_inputs: false |
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group_by_length: false |
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#bf16: auto |
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#fp16: true |
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#tf32: false |
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float16: true |
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flash_attn: |
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xformers_attention: true |
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optimizer: paged_adamw_8bit |
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gpu_memory_limit: 8GiB |
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hub_model_id: jtatman/pythia-160m-storytelling |
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early_stopping_patience: 2 |
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#resume_from_checkpoint: outputs/lora-alpaca-pythia-125m/checkpoint-51040 |
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auto_resume_from_checkpoints: true |
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local_rank: |
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weight_decay: 0.0 |
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#evals_per_epoch: 4 |
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eval_steps: 200 |
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logging_steps: 1 |
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save_steps: 200 |
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save_total_limit: 5 |
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warmup_steps: 100 |
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tokens: |
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- "[INST]" |
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- "[/INST]" |
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``` |
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</details><br> |
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# pythia-160m-storytelling |
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This model is a fine-tuned version of [EleutherAI/pythia-160m-deduped](https://huggingface.co/EleutherAI/pythia-160m-deduped) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 5.0363 |
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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: 0.0006 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine_with_restarts |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 5.5185 | 0.0012 | 1 | 4.8238 | |
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| 4.2012 | 0.2348 | 200 | 4.1556 | |
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| 4.4185 | 0.4696 | 400 | 4.8159 | |
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| 5.0973 | 0.7043 | 600 | 5.0363 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
