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--- |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: premai-io/prem-1B |
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model-index: |
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- name: prem-1B-32k |
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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: `0.4.0` |
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```yaml |
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base_model: premai-io/prem-1B |
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model_type: LlamaForCausalLM |
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tokenizer_type: AutoTokenizer |
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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: argilla/distilabel-capybara-dpo-7k-binarized |
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type: orpo.chat_template |
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dataset_prepared_path: last_run_prepared |
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val_set_size: 0.001 |
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output_dir: ./prem-1B-32k |
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save_safetensors: true |
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sequence_len: 8192 |
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sample_packing: false |
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pad_to_sequence_len: false |
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use_pose: true |
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pose_max_context_len: 262144 |
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min_sample_len: 6144 |
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pose_num_chunks: 16 |
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curriculum_sampling: true |
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overrides_of_model_config: |
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rope_theta: 500000.0 |
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max_position_embeddings: 262144 |
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# peft_use_dora: true |
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adapter: lora |
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peft_use_rslora: true |
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lora_model_dir: |
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lora_r: 1024 |
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lora_alpha: 1024 |
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lora_dropout: 0.1 |
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lora_target_modules: |
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- q_proj |
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- k_proj |
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- v_proj |
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- o_proj |
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lora_modules_to_save: |
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- embed_tokens |
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- lm_head |
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wandb_project: |
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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: 8 |
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micro_batch_size: 1 |
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num_epochs: 20 |
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optimizer: adamw_bnb_8bit |
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lr_scheduler: cosine |
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learning_rate: 0.00001 |
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max_grad_norm: 1.0 |
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adam_beta2: 0.95 |
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train_on_inputs: false |
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group_by_length: false |
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bf16: true |
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fp16: |
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tf32: false |
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gradient_checkpointing: true |
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gradient_checkpointing_kwargs: |
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use_reentrant: 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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sdp_attention: |
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s2_attention: |
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warmup_steps: 10 |
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evals_per_epoch: 8 |
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saves_per_epoch: 8 |
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debug: |
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deepspeed: |
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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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pad_token: <|end_of_text|> |
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``` |
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</details><br> |
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# prem-1B-32k |
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This model is a fine-tuned version of [premai-io/prem-1B](https://huggingface.co/premai-io/prem-1B) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0059 |
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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: 1e-05 |
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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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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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- total_eval_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.95) 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: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.7672 | 1.0 | 1 | 3.0074 | |
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| 0.7672 | 2.0 | 2 | 2.6057 | |
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| 0.7422 | 3.0 | 3 | 2.2898 | |
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| 0.7211 | 4.0 | 4 | 2.1453 | |
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| 0.6591 | 5.0 | 5 | 1.6360 | |
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| 0.4514 | 6.0 | 6 | 0.7589 | |
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| 0.24 | 7.0 | 7 | 0.6621 | |
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| 0.1584 | 8.0 | 8 | 0.8121 | |
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| 0.1235 | 9.0 | 9 | 0.7538 | |
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| 0.0998 | 10.0 | 10 | 0.7743 | |
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| 0.0869 | 11.0 | 11 | 0.7771 | |
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| 0.1692 | 12.0 | 12 | 0.8293 | |
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| 0.0702 | 13.0 | 13 | 0.8939 | |
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| 0.063 | 14.0 | 14 | 0.9582 | |
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| 0.0567 | 15.0 | 15 | 0.9825 | |
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| 0.052 | 16.0 | 16 | 0.9960 | |
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| 0.0488 | 17.0 | 17 | 0.9883 | |
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| 0.0457 | 18.0 | 18 | 1.0004 | |
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| 0.0436 | 19.0 | 19 | 1.0056 | |
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| 0.0427 | 20.0 | 20 | 1.0059 | |
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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 2.1.2+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
