End of training
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| 1 | 
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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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            - axolotl
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            - generated_from_trainer
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            base_model: meta-llama/Meta-Llama-3-8B
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            model-index:
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            - name: isafpr-llama3-lora
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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
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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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            +
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            data_seed: 42
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            seed: 42
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            datasets:
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              - path: data/isaf_press_releases_ft.jsonl
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                conversation: alpaca
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                type: sharegpt
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            dataset_prepared_path:
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            val_set_size: 0.05
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            output_dir: ./outputs/llama3/lora-out
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            hub_model_id: strickvl/isafpr-llama3-lora
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            sequence_len: 2048
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            sample_packing: true
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            eval_sample_packing: false
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            pad_to_sequence_len: true
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            adapter: lora
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            lora_model_dir:
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            lora_r: 32
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            lora_alpha: 16
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            lora_dropout: 0.05
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            lora_target_linear: true
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            lora_fan_in_fan_out:
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            lora_modules_to_save:
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              - embed_tokens
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              - lm_head
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            +
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            wandb_project: isaf_pr_ft
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            wandb_entity: strickvl
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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: 4
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            micro_batch_size: 2
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            num_epochs: 4
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            optimizer: adamw_bnb_8bit
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            lr_scheduler: cosine
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            learning_rate: 0.0002
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            +
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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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            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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            s2_attention:
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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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            eval_max_new_tokens: 128
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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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            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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            # isafpr-llama3-lora
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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 None dataset.
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            It achieves the following results on the evaluation set:
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            - Loss: 0.0371
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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: 2
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            - gradient_accumulation_steps: 4
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            - total_train_batch_size: 16
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            - total_eval_batch_size: 4
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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: 4
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            ### Training results
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            | Training Loss | Epoch  | Step | Validation Loss |
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            |:-------------:|:------:|:----:|:---------------:|
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            | 2.0023        | 0.0173 | 1    | 2.0120          |
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            | 0.0975        | 0.2597 | 15   | 0.0792          |
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            | 0.0576        | 0.5195 | 30   | 0.0586          |
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            | 0.0317        | 0.7792 | 45   | 0.0476          |
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            | 0.0367        | 1.0390 | 60   | 0.0445          |
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            | 0.0315        | 1.2078 | 75   | 0.0421          |
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            | 0.0249        | 1.4675 | 90   | 0.0429          |
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            | 0.0302        | 1.7273 | 105  | 0.0380          |
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            | 0.0264        | 1.9870 | 120  | 0.0376          |
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            | 0.0184        | 2.1515 | 135  | 0.0362          |
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            | 0.0174        | 2.4113 | 150  | 0.0366          |
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            | 0.0152        | 2.6710 | 165  | 0.0373          |
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            | 0.016         | 2.9307 | 180  | 0.0361          |
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            | 0.0128        | 3.0996 | 195  | 0.0361          |
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            | 0.0172        | 3.3593 | 210  | 0.0369          |
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            | 0.0086        | 3.6190 | 225  | 0.0371          |
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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+cu121
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            - Datasets 2.19.1
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            - Tokenizers 0.19.1
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            version https://git-lfs.github.com/spec/v1
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            oid sha256:2c60ca31b2665cd21934111a89f2d862cef54f73710d4dcf78fee232fe4726b6
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            size 2437053202
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