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README.md

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----
-library_name: peft
-base_model: NousResearch/CodeLlama-13b-hf
-tags:
-- axolotl
-- generated_from_trainer
-model-index:
-- name: 01209bf5-5487-42c2-b494-ead45201ea0e
-  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/CodeLlama-13b-hf
-bf16: auto
-chat_template: llama3
-dataset_prepared_path: null
-datasets:
-- data_files:
-  - 9a8514bc9995e10c_train_data.json
-  ds_type: json
-  format: custom
-  path: /workspace/input_data/9a8514bc9995e10c_train_data.json
-  type:
-    field_instruction: premise
-    field_output: hypothesis
-    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: marialvsantiago/01209bf5-5487-42c2-b494-ead45201ea0e
-hub_repo: null
-hub_strategy: end
-hub_token: null
-learning_rate: 0.0002
-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: 78GiB
-max_steps: 30
-micro_batch_size: 2
-mlflow_experiment_name: /tmp/9a8514bc9995e10c_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: ab917f13-90ab-4a3a-9e38-f2d73001d41f
-wandb_project: Gradients-On-Demand
-wandb_run: your_name
-wandb_runid: ab917f13-90ab-4a3a-9e38-f2d73001d41f
-warmup_steps: 5
-weight_decay: 0.01
-xformers_attention: true
-
-```
-
-</details><br>
-
-# 01209bf5-5487-42c2-b494-ead45201ea0e
-
-This model is a fine-tuned version of [NousResearch/CodeLlama-13b-hf](https://huggingface.co/NousResearch/CodeLlama-13b-hf) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 2.3543
-
-## 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: 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        | 0.0002 | 1    | 3.2874          |
-| 12.5508       | 0.0008 | 5    | 3.0211          |
-| 10.5244       | 0.0017 | 10   | 2.5698          |
-| 10.5628       | 0.0025 | 15   | 2.4511          |
-| 9.5879        | 0.0034 | 20   | 2.3925          |
-| 10.0115       | 0.0042 | 25   | 2.3596          |
-| 8.7842        | 0.0051 | 30   | 2.3543          |
-
-
-### Framework versions
-
-- PEFT 0.13.2
-- Transformers 4.46.0
-- Pytorch 2.5.0+cu124
-- Datasets 3.0.1
-- Tokenizers 0.20.1