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--- |
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license: llama3 |
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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: meta-llama/Meta-Llama-3-70B-Instruct |
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model-index: |
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- name: output/llama3-70b |
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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: meta-llama/Meta-Llama-3-70B-Instruct |
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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: true |
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strict: false |
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datasets: |
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- path: awilliamson/qbank_conversations |
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type: chat_template |
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chat_template: llama3 |
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field_messages: conversations |
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message_field_role: from |
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message_field_content: value |
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roles: |
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system: |
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- system |
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user: |
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- user |
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assistant: |
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- assistant |
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chat_template: llama3 |
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adapter: qlora |
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lora_r: 128 |
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lora_alpha: 32 |
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lora_modules_to_save: [embed_tokens, lm_head] |
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lora_dropout: 0.05 |
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lora_target_linear: true |
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dataset_prepared_path: last_run_prepared |
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val_set_size: 0.05 |
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output_dir: ./output/llama3-70b |
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sequence_len: 2048 |
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sample_packing: false |
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pad_to_sequence_len: true |
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wandb_project: llama-70b |
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wandb_watch: |
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wandb_run_id: |
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wandb_log_model: |
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gradient_accumulation_steps: 4 |
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micro_batch_size: 2 |
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num_epochs: 3 |
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optimizer: adamw_torch |
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lr_scheduler: cosine |
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learning_rate: 2e-4 |
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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: |
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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: false |
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early_stopping_patience: |
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resume_from_checkpoint: |
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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_steps: 0 |
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evals_per_epoch: 5 |
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eval_table_size: |
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saves_per_epoch: 1 |
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save_total_limit: 10 |
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save_steps: |
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debug: |
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weight_decay: 0.00 |
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fsdp: |
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- full_shard |
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- auto_wrap |
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fsdp_config: |
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fsdp_limit_all_gathers: true |
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fsdp_sync_module_states: true |
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fsdp_offload_params: true |
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fsdp_use_orig_params: false |
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fsdp_cpu_ram_efficient_loading: true |
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fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP |
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fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer |
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fsdp_state_dict_type: FULL_STATE_DICT |
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fsdp_sharding_strategy: FULL_SHARD |
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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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# output/llama3-70b |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5806 |
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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: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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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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- 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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| 1.6238 | 0.0769 | 1 | 1.6328 | |
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| 1.2354 | 0.2308 | 3 | 1.6006 | |
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| 1.1512 | 0.4615 | 6 | 1.6043 | |
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| 1.1183 | 0.6923 | 9 | 1.5402 | |
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| 1.0818 | 0.9231 | 12 | 1.4909 | |
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| 0.7404 | 1.1538 | 15 | 1.4745 | |
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| 0.6681 | 1.3846 | 18 | 1.5023 | |
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| 0.6163 | 1.6154 | 21 | 1.5385 | |
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| 0.6596 | 1.8462 | 24 | 1.5612 | |
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| 0.5081 | 2.0769 | 27 | 1.5699 | |
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| 0.5118 | 2.3077 | 30 | 1.5786 | |
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| 0.4827 | 2.5385 | 33 | 1.5808 | |
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| 0.4768 | 2.7692 | 36 | 1.5800 | |
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| 0.484 | 3.0 | 39 | 1.5806 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.41.1 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
