End of training

README.md

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+---
+library_name: peft
+license: apache-2.0
+base_model: JackFram/llama-160m
+tags:
+- axolotl
+- generated_from_trainer
+model-index:
+- name: 6d24fd38-8438-4c47-884b-f2aaf116228e
+  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: JackFram/llama-160m
+bf16: auto
+chat_template: llama3
+dataset_prepared_path: null
+datasets:
+- data_files:
+  - 2d15b3651d2b81fd_train_data.json
+  ds_type: json
+  format: custom
+  path: /workspace/input_data/2d15b3651d2b81fd_train_data.json
+  type:
+    field_instruction: INSTRUCTION
+    field_output: RESPONSE
+    format: '{instruction}'
+    no_input_format: '{instruction}'
+    system_format: '{system}'
+    system_prompt: ''
+debug: null
+deepspeed: null
+device: cuda
+early_stopping_patience: 1
+eval_max_new_tokens: 128
+eval_steps: 5
+eval_table_size: null
+evals_per_epoch: null
+flash_attention: false
+fp16: null
+gradient_accumulation_steps: 4
+gradient_checkpointing: true
+group_by_length: false
+hub_model_id: vermoney/6d24fd38-8438-4c47-884b-f2aaf116228e
+hub_repo: null
+hub_strategy: checkpoint
+hub_token: null
+learning_rate: 0.0001
+load_in_4bit: false
+load_in_8bit: false
+local_rank: null
+logging_steps: 3
+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_memory:
+  0: 49GiB
+max_steps: 30
+micro_batch_size: 2
+mlflow_experiment_name: /tmp/2d15b3651d2b81fd_train_data.json
+model_type: AutoModelForCausalLM
+num_epochs: 1
+optimizer: adamw_torch
+output_dir: miner_id_24
+pad_to_sequence_len: true
+resume_from_checkpoint: null
+s2_attention: null
+sample_packing: false
+save_steps: 10
+sequence_len: 1024
+special_tokens:
+  pad_token: </s>
+strict: false
+tf32: false
+tokenizer_type: AutoTokenizer
+train_on_inputs: true
+trust_remote_code: true
+val_set_size: 0.05
+wandb_entity: null
+wandb_mode: online
+wandb_name: d86c16ef-47f4-473a-825c-80ba0605c24d
+wandb_project: Gradients-On-Demand
+wandb_run: your_name
+wandb_runid: d86c16ef-47f4-473a-825c-80ba0605c24d
+warmup_steps: 5
+weight_decay: 0.01
+xformers_attention: true
+
+```
+
+</details><br>
+
+# 6d24fd38-8438-4c47-884b-f2aaf116228e
+
+This model is a fine-tuned version of [JackFram/llama-160m](https://huggingface.co/JackFram/llama-160m) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 2.5441
+
+## 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: 0.0001
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 8
+- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 5
+- training_steps: 30
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| No log        | 0.0010 | 1    | 2.7682          |
+| 2.6436        | 0.0049 | 5    | 2.7519          |
+| 2.6707        | 0.0098 | 10   | 2.6648          |
+| 2.5581        | 0.0147 | 15   | 2.6013          |
+| 2.5049        | 0.0196 | 20   | 2.5635          |
+| 2.4564        | 0.0245 | 25   | 2.5471          |
+| 2.5075        | 0.0294 | 30   | 2.5441          |
+
+
+### 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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