| --- |
| library_name: peft |
| base_model: Vikhrmodels/Vikhr-7B-instruct_0.4 |
| tags: |
| - axolotl |
| - generated_from_trainer |
| model-index: |
| - name: 50089649-6ad2-4906-b3ec-a7a2fc7302c4 |
| 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: Vikhrmodels/Vikhr-7B-instruct_0.4 |
| bf16: auto |
| chat_template: llama3 |
| dataloader_num_workers: 12 |
| dataset_prepared_path: null |
| datasets: |
| - data_files: |
| - c75bfa6e12efc2e1_train_data.json |
| ds_type: json |
| format: custom |
| path: /workspace/input_data/ |
| type: |
| field_input: content |
| field_instruction: subject |
| field_output: message |
| format: '{instruction} {input}' |
| no_input_format: '{instruction}' |
| system_format: '{system}' |
| system_prompt: '' |
| debug: null |
| deepspeed: null |
| early_stopping_patience: null |
| eval_max_new_tokens: 128 |
| eval_steps: null |
| eval_table_size: null |
| evals_per_epoch: null |
| flash_attention: true |
| fp16: null |
| fsdp: null |
| fsdp_config: null |
| gradient_accumulation_steps: 4 |
| gradient_checkpointing: false |
| group_by_length: false |
| hub_model_id: aleegis/50089649-6ad2-4906-b3ec-a7a2fc7302c4 |
| 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: null |
| lora_alpha: 32 |
| lora_dropout: 0.15 |
| lora_fan_in_fan_out: null |
| lora_model_dir: null |
| lora_r: 32 |
| lora_target_linear: true |
| loraplus_lr_embedding: 1.0e-06 |
| loraplus_lr_ratio: 16 |
| lr_scheduler: cosine |
| max_grad_norm: 1 |
| max_steps: 1500 |
| micro_batch_size: 2 |
| mlflow_experiment_name: /tmp/c75bfa6e12efc2e1_train_data.json |
| model_type: AutoModelForCausalLM |
| num_epochs: 200 |
| optimizer: adamw_torch_fused |
| output_dir: miner_id_24 |
| pad_to_sequence_len: true |
| resume_from_checkpoint: null |
| s2_attention: null |
| sample_packing: false |
| save_steps: null |
| save_total_limit: 10 |
| saves_per_epoch: 0 |
| sequence_len: 1024 |
| strict: false |
| tf32: true |
| tokenizer_type: AutoTokenizer |
| train_on_inputs: false |
| trust_remote_code: true |
| val_set_size: 0.0 |
| wandb_entity: null |
| wandb_mode: online |
| wandb_name: a9081eb3-0860-4935-ab4e-1d3142884165 |
| wandb_project: Gradients-On-Demand |
| wandb_run: your_name |
| wandb_runid: a9081eb3-0860-4935-ab4e-1d3142884165 |
| warmup_steps: 100 |
| weight_decay: 0 |
| xformers_attention: null |
| |
| ``` |
|
|
| </details><br> |
|
|
| # 50089649-6ad2-4906-b3ec-a7a2fc7302c4 |
|
|
| This model is a fine-tuned version of [Vikhrmodels/Vikhr-7B-instruct_0.4](https://huggingface.co/Vikhrmodels/Vikhr-7B-instruct_0.4) on the None dataset. |
|
|
| ## 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_FUSED 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: 100 |
| - training_steps: 1500 |
| |
| ### Training results |
| |
| |
| |
| ### Framework versions |
| |
| - PEFT 0.13.2 |
| - Transformers 4.46.0 |
| - Pytorch 2.5.0+cu124 |
| - Datasets 3.0.1 |
| - Tokenizers 0.20.1 |
