End of training

README.md

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+---
+library_name: peft
+license: other
+base_model: NousResearch/Meta-Llama-3-8B
+tags:
+- axolotl
+- generated_from_trainer
+model-index:
+- name: aa4061e1-ca3b-4425-b7b5-a1c7885499ef
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+[<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)
+<details><summary>See axolotl config</summary>
+
+axolotl version: `0.4.1`
+```yaml
+adapter: lora
+base_model: NousResearch/Meta-Llama-3-8B
+bf16: false
+chat_template: llama3
+dataset_prepared_path: null
+datasets:
+- data_files:
+  - 3ebce502efc44504_train_data.json
+  ds_type: json
+  format: custom
+  path: /workspace/input_data/3ebce502efc44504_train_data.json
+  type:
+    field_input: input
+    field_instruction: question
+    field_output: answer_text
+    format: '{instruction} {input}'
+    no_input_format: '{instruction}'
+    system_format: '{system}'
+    system_prompt: ''
+debug: null
+deepspeed: null
+devices:
+- 0
+- 1
+- 2
+- 3
+- 4
+- 5
+- 6
+- 7
+early_stopping_patience: null
+eval_max_new_tokens: 128
+eval_table_size: null
+evals_per_epoch: 4
+flash_attention: true
+fp16: true
+fsdp: null
+fsdp_config: null
+gradient_accumulation_steps: 4
+gradient_checkpointing: false
+group_by_length: false
+hub_model_id: jssky/aa4061e1-ca3b-4425-b7b5-a1c7885499ef
+hub_repo: null
+hub_strategy: checkpoint
+hub_token: null
+learning_rate: 0.0002
+load_in_4bit: false
+load_in_8bit: false
+local_rank: null
+logging_steps: 1
+lora_alpha: 32
+lora_dropout: 0.05
+lora_fan_in_fan_out: null
+lora_model_dir: null
+lora_r: 16
+lora_target_linear: true
+lr_scheduler: cosine
+max_steps: 10
+micro_batch_size: 1
+mlflow_experiment_name: /tmp/3ebce502efc44504_train_data.json
+model_type: AutoModelForCausalLM
+num_epochs: 1
+num_gpus: 8
+optimizer: adamw_bnb_8bit
+output_dir: miner_id_24
+pad_to_sequence_len: true
+resume_from_checkpoint: null
+s2_attention: null
+sample_packing: false
+saves_per_epoch: 4
+sequence_len: 4056
+special_tokens:
+  pad_token: <|end_of_text|>
+strict: false
+tf32: false
+tokenizer_type: AutoTokenizer
+train_on_inputs: false
+trust_remote_code: true
+val_set_size: 0.05
+wandb_entity: null
+wandb_mode: online
+wandb_name: aa4061e1-ca3b-4425-b7b5-a1c7885499ef
+wandb_project: Gradients-On-Demand
+wandb_run: your_name
+wandb_runid: aa4061e1-ca3b-4425-b7b5-a1c7885499ef
+warmup_steps: 10
+weight_decay: 0.0
+xformers_attention: null
+
+```
+
+</details><br>
+
+# aa4061e1-ca3b-4425-b7b5-a1c7885499ef
+
+This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1496
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0002
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 4
+- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 10
+- training_steps: 10
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 0.4669        | 0.0006 | 1    | 0.9723          |
+| 0.9745        | 0.0017 | 3    | 0.7737          |
+| 0.3239        | 0.0034 | 6    | 0.2285          |
+| 0.0497        | 0.0051 | 9    | 0.1496          |
+
+
+### Framework versions
+
+- PEFT 0.13.2
+- Transformers 4.46.0
+- Pytorch 2.5.0+cu124
+- Datasets 3.0.1
+- Tokenizers 0.20.1

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