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--- |
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language: |
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- en |
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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-3-8B-Instruct |
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datasets: |
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- Norquinal/claude_multi_instruct_30k |
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model-index: |
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- name: llama-3-8b-claudstruct-v3 |
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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-3-8B-Instruct |
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model_type: LlamaForCausalLM |
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tokenizer_type: AutoTokenizer # PreTrainedTokenizerFast |
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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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chat_template: llama3 |
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datasets: |
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- path: Norquinal/claude_multi_instruct_30k |
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type: alpaca |
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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: ./outputs/llama-3-8b-claudstruct-v3/ |
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adapter: qlora |
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lora_model_dir: |
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sequence_len: 512 |
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sample_packing: false |
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pad_to_sequence_len: true |
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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_modules: |
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lora_target_linear: true |
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lora_fan_in_fan_out: |
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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: 1 |
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micro_batch_size: 8 |
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num_epochs: 2 |
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optimizer: adamw_torch |
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lr_scheduler: cosine |
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learning_rate: 0.00001 |
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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: 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_steps: 10 |
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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: |
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weight_decay: 0.0 |
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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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# llama-3-8b-claudstruct-v3 |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the [Norquinal/claude_multi_instruct_30k](https://huggingface.co/datasets/Norquinal/claude_multi_instruct_30k) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.6226 |
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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: 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: 2 |
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- total_train_batch_size: 16 |
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- total_eval_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 |
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- lr_scheduler_warmup_steps: 10 |
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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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| 2.2209 | 0.0007 | 1 | 2.0399 | |
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| 1.7842 | 0.2502 | 341 | 1.6960 | |
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| 1.6914 | 0.5004 | 682 | 1.6590 | |
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| 1.6757 | 0.7506 | 1023 | 1.6414 | |
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| 1.5182 | 1.0007 | 1364 | 1.6319 | |
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| 1.8421 | 1.2509 | 1705 | 1.6264 | |
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| 1.7271 | 1.5011 | 2046 | 1.6237 | |
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| 1.4817 | 1.7513 | 2387 | 1.6226 | |
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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 2.3.0 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_jrahn__llama-3-8b-claudstruct-v3) |
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |65.62| |
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|AI2 Reasoning Challenge (25-Shot)|58.96| |
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|HellaSwag (10-Shot) |80.05| |
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|MMLU (5-Shot) |64.55| |
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|TruthfulQA (0-shot) |51.76| |
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|Winogrande (5-shot) |74.19| |
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|GSM8k (5-shot) |64.22| |
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