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README.md
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---
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library_name: transformers
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license: llama3
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base_model: meta-llama/Meta-Llama-3-8B
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tags:
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- axolotl
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- generated_from_trainer
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model-index:
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- name: L3-Pneuma-8B
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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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/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
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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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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: Kquant03/Sandevistan_Reformat
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type: customllama3_stan
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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/out
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max_steps: 80000
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fix_untrained_tokens: true
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sequence_len: 4096
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sample_packing: true
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pad_to_sequence_len: true
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wandb_project: Pneuma
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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: 16
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micro_batch_size: 8
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num_epochs: 1
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optimizer: paged_adamw_8bit
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lr_scheduler: cosine
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learning_rate: 0.00001
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max_grad_norm: 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:
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tf32: false
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gradient_checkpointing: unsloth
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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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eval_sample_packing: false
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plugins:
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- axolotl.integrations.liger.LigerPlugin
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liger_rope: true
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liger_rms_norm: true
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liger_swiglu: true
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liger_fused_linear_cross_entropy: true
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hub_model_id: Replete-AI/L3-Pneuma-8B
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hub_strategy: every_save
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warmup_steps: 10
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evals_per_epoch: 3
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eval_table_size:
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saves_per_epoch: 3
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debug:
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deepspeed:
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weight_decay: 0.1
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fsdp:
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fsdp_config:
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special_tokens:
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bos_token: "<|begin_of_text|>"
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eos_token: "<|end_of_text|>"
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pad_token: "<|end_of_text|>"
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tokens:
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```
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</details><br>
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# L3-Pneuma-8B
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the [Sandevistan](https://huggingface.co/datasets/Replete-AI/Sandevistan) dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.7381
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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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- gradient_accumulation_steps: 16
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- total_train_batch_size: 128
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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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- training_steps: 743
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 1.0378 | 0.0013 | 1 | 3.0437 |
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| 0.6816 | 0.3334 | 248 | 2.7341 |
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| 0.6543 | 0.6667 | 496 | 2.7381 |
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### Framework versions
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- Transformers 4.45.1
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- Pytorch 2.3.1+cu121
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- Datasets 2.21.0
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- Tokenizers 0.20.1
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