Instructions to use Dnsx077/f3c7fe3f-f074-4117-ab17-480d72c938d9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Dnsx077/f3c7fe3f-f074-4117-ab17-480d72c938d9 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/CodeLlama-7b-hf") model = PeftModel.from_pretrained(base_model, "Dnsx077/f3c7fe3f-f074-4117-ab17-480d72c938d9") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| base_model: NousResearch/CodeLlama-7b-hf | |
| tags: | |
| - axolotl | |
| - generated_from_trainer | |
| model-index: | |
| - name: f3c7fe3f-f074-4117-ab17-480d72c938d9 | |
| 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-7b-hf | |
| bf16: auto | |
| chat_template: llama3 | |
| dataset_prepared_path: null | |
| datasets: | |
| - data_files: | |
| - 9ff1d2b7724bd741_train_data.json | |
| ds_type: json | |
| format: custom | |
| path: /workspace/input_data/9ff1d2b7724bd741_train_data.json | |
| type: | |
| field_instruction: question | |
| field_output: query | |
| format: '{instruction}' | |
| no_input_format: '{instruction}' | |
| system_format: '{system}' | |
| system_prompt: '' | |
| debug: null | |
| deepspeed: null | |
| early_stopping_patience: 1 | |
| eval_max_new_tokens: 128 | |
| eval_steps: 25 | |
| eval_table_size: null | |
| flash_attention: false | |
| fp16: false | |
| fsdp: null | |
| fsdp_config: null | |
| gradient_accumulation_steps: 4 | |
| gradient_checkpointing: true | |
| group_by_length: true | |
| hub_model_id: Dnsx077/f3c7fe3f-f074-4117-ab17-480d72c938d9 | |
| 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: 50 | |
| micro_batch_size: 2 | |
| mlflow_experiment_name: /tmp/9ff1d2b7724bd741_train_data.json | |
| model_type: AutoModelForCausalLM | |
| num_epochs: 3 | |
| 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: 25 | |
| sequence_len: 4056 | |
| special_tokens: | |
| pad_token: </s> | |
| strict: false | |
| tf32: false | |
| tokenizer_type: AutoTokenizer | |
| train_on_inputs: false | |
| trust_remote_code: true | |
| val_set_size: 0.05 | |
| wandb_entity: taoxminer-education | |
| wandb_mode: online | |
| wandb_name: f3c7fe3f-f074-4117-ab17-480d72c938d9 | |
| wandb_project: Gradients-On-Demand | |
| wandb_run: taoxminer | |
| wandb_runid: f3c7fe3f-f074-4117-ab17-480d72c938d9 | |
| warmup_ratio: 0.05 | |
| weight_decay: 0.01 | |
| xformers_attention: true | |
| ``` | |
| </details><br> | |
| # f3c7fe3f-f074-4117-ab17-480d72c938d9 | |
| This model is a fine-tuned version of [NousResearch/CodeLlama-7b-hf](https://huggingface.co/NousResearch/CodeLlama-7b-hf) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.5707 | |
| ## 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: 2 | |
| - training_steps: 50 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 2.6542 | 0.0011 | 1 | 2.0670 | | |
| | 2.1683 | 0.0269 | 25 | 0.6165 | | |
| | 1.8088 | 0.0537 | 50 | 0.5707 | | |
| ### Framework versions | |
| - PEFT 0.13.2 | |
| - Transformers 4.46.0 | |
| - Pytorch 2.5.0+cu124 | |
| - Datasets 3.0.1 | |
| - Tokenizers 0.20.1 |