End of training

README.md

@@ -1,158 +0,0 @@
----
-library_name: peft
-license: mit
-base_model: NousResearch/Nous-Capybara-7B-V1
-tags:
-- axolotl
-- generated_from_trainer
-model-index:
-- name: e156e6c6-eb07-4279-9ee1-b1737317c4e8
-  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
-auto_find_batch_size: true
-base_model: NousResearch/Nous-Capybara-7B-V1
-bf16: auto
-chat_template: llama3
-dataset_prepared_path: null
-datasets:
-- data_files:
-  - eb740de30ff7fe93_train_data.json
-  ds_type: json
-  format: custom
-  path: /workspace/input_data/eb740de30ff7fe93_train_data.json
-  type:
-    field_input: title
-    field_instruction: question
-    field_output: answer
-    format: '{instruction} {input}'
-    no_input_format: '{instruction}'
-    system_format: '{system}'
-    system_prompt: ''
-debug: null
-deepspeed: null
-do_eval: true
-early_stopping_patience: 3
-eval_max_new_tokens: 128
-eval_steps: 50
-evals_per_epoch: null
-flash_attention: true
-fp16: false
-fsdp: null
-fsdp_config: null
-gradient_accumulation_steps: 2
-gradient_checkpointing: false
-group_by_length: true
-hub_model_id: lesso18/e156e6c6-eb07-4279-9ee1-b1737317c4e8
-hub_repo: null
-hub_strategy: checkpoint
-hub_token: null
-learning_rate: 0.000218
-load_in_4bit: false
-load_in_8bit: false
-local_rank: null
-logging_steps: 10
-lora_alpha: 128
-lora_dropout: 0.05
-lora_fan_in_fan_out: null
-lora_model_dir: null
-lora_r: 64
-lora_target_linear: true
-lr_scheduler: cosine
-max_grad_norm: 1.0
-max_steps: 500
-micro_batch_size: 4
-mlflow_experiment_name: /tmp/G.O.D/eb740de30ff7fe93_train_data.json
-model_type: AutoModelForCausalLM
-num_epochs: 1
-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: 50
-saves_per_epoch: null
-seed: 180
-sequence_len: 512
-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: e2c64dd8-ac0d-4315-9953-54dcef591325
-wandb_project: 18a
-wandb_run: your_name
-wandb_runid: e2c64dd8-ac0d-4315-9953-54dcef591325
-warmup_steps: 50
-weight_decay: 0.0
-xformers_attention: null
-
-```
-
-</details><br>
-
-# e156e6c6-eb07-4279-9ee1-b1737317c4e8
-
-This model is a fine-tuned version of [NousResearch/Nous-Capybara-7B-V1](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1) on the None dataset.
-It achieves the following results on the evaluation set:
-- Loss: 2.1722
-
-## 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: 0.000218
-- train_batch_size: 4
-- eval_batch_size: 4
-- seed: 180
-- gradient_accumulation_steps: 2
-- total_train_batch_size: 8
-- 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: 50
-- training_steps: 500
-
-### Training results
-
-| Training Loss | Epoch  | Step | Validation Loss |
-|:-------------:|:------:|:----:|:---------------:|
-| No log        | 0.0002 | 1    | 2.9893          |
-| 2.3877        | 0.0121 | 50   | 2.1539          |
-| 2.2267        | 0.0241 | 100  | 2.3052          |
-| 2.1741        | 0.0362 | 150  | 2.3203          |
-| 2.2808        | 0.0483 | 200  | 2.1722          |
-
-
-### Framework versions
-
-- PEFT 0.13.2
-- Transformers 4.46.0
-- Pytorch 2.5.0+cu124
-- Datasets 3.0.1
-- Tokenizers 0.20.1