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README.md

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----
-library_name: peft
-base_model: Xenova/tiny-random-Phi3ForCausalLM
-tags:
-- axolotl
-- generated_from_trainer
-model-index:
-- name: effd8ea5-82c2-48f4-99e9-491333bb8dfe
-  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: Xenova/tiny-random-Phi3ForCausalLM
-bf16: true
-chat_template: llama3
-data_processes: 8
-dataset_prepared_path: null
-datasets:
-- data_files:
-  - 25c07aed58d15eae_train_data.json
-  ds_type: json
-  format: custom
-  path: /workspace/input_data/25c07aed58d15eae_train_data.json
-  type:
-    field_instruction: instruction
-    field_output: output
-    format: '{instruction}'
-    no_input_format: '{instruction}'
-    system_format: '{system}'
-    system_prompt: ''
-debug: null
-deepspeed: null
-device_map: auto
-do_eval: true
-early_stopping_patience: 1
-eval_batch_size: 4
-eval_max_new_tokens: 128
-eval_steps: 5
-eval_table_size: null
-evals_per_epoch: null
-flash_attention: false
-fp16: false
-gradient_accumulation_steps: 4
-gradient_checkpointing: true
-group_by_length: true
-hub_model_id: vermoney/effd8ea5-82c2-48f4-99e9-491333bb8dfe
-hub_repo: null
-hub_strategy: end
-hub_token: null
-learning_rate: 0.0001
-load_in_4bit: false
-load_in_8bit: false
-local_rank: null
-logging_steps: 1
-lora_alpha: 64
-lora_dropout: 0.05
-lora_fan_in_fan_out: null
-lora_model_dir: null
-lora_r: 32
-lora_target_linear: true
-lr_scheduler: cosine
-max_grad_norm: 1.0
-max_memory:
-  0: 78GiB
-  cpu: 30GiB
-max_steps: 30
-micro_batch_size: 4
-mlflow_experiment_name: /tmp/25c07aed58d15eae_train_data.json
-model_type: AutoModelForCausalLM
-num_epochs: 1
-optim_args:
-  adam_beta1: 0.9
-  adam_beta2: 0.95
-  adam_epsilon: 1e-5
-optimizer: adamw_bnb_8bit
-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
-strict: false
-tf32: true
-tokenizer_type: AutoTokenizer
-train_on_inputs: false
-trust_remote_code: true
-val_set_size: 0.05
-wandb_entity: null
-wandb_mode: online
-wandb_name: 39e3f072-d312-4fef-b3e3-76f2ed05f62d
-wandb_project: cold30
-wandb_run: your_name
-wandb_runid: 39e3f072-d312-4fef-b3e3-76f2ed05f62d
-warmup_steps: 5
-weight_decay: 0.001
-xformers_attention: null
-
-```
-
-</details><br>
-
-# effd8ea5-82c2-48f4-99e9-491333bb8dfe
-
-This model is a fine-tuned version of [Xenova/tiny-random-Phi3ForCausalLM](https://huggingface.co/Xenova/tiny-random-Phi3ForCausalLM) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: nan
-
-## 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: 4
-- eval_batch_size: 4
-- seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 16
-- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_steps: 5
-- training_steps: 30
-
-### Training results
-
-| Training Loss | Epoch  | Step | Validation Loss |
-|:-------------:|:------:|:----:|:---------------:|
-| 0.0           | 0.0003 | 1    | nan             |
-| 0.0           | 0.0016 | 5    | nan             |
-| 0.0           | 0.0033 | 10   | nan             |
-| 0.0           | 0.0049 | 15   | nan             |
-
-
-### Framework versions
-
-- PEFT 0.13.2
-- Transformers 4.46.0
-- Pytorch 2.5.0+cu124
-- Datasets 3.0.1
-- Tokenizers 0.20.1