--- license: apache-2.0 library_name: peft tags: - axolotl - generated_from_trainer base_model: mistralai/Mistral-7B-v0.1 model-index: - name: mistral-tenglish-april5_2 results: [] --- [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config axolotl version: `0.4.0` ```yaml base_model: mistralai/Mistral-7B-v0.1 base_model_config: mistralai/Mistral-7B-v0.1 model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer is_mistral_derived_model: true load_in_8bit: true load_in_4bit: false bf16: true fp16: false tf32: false bfloat16: true datasets: - path: indiehackers/telugu_romanized_2048_mistral type: completion field: text #dataset_prepared_path: ./dataset_tt23 hub_model_id: indiehackers/mistral-tenglish-april5_2 hf_use_auth_token: true val_set_size: 0.0 sequence_len: 2048 pad_to_sequence_len: true sample_packing: true # eval_sample_packing: false adapter: lora lora_r: 128 lora_alpha: 256 lora_dropout: 0.05 lora_target_linear: true wandb_project: mistral-tenglish wandb_entity: team-nik #wandb_log_model: end output_dir: ./mistral-tenglish-out # Training hyperparameters gradient_accumulation_steps: 2 micro_batch_size: 7 warmup_steps: 50 learning_rate: 0.00002 logging_steps: 1 evals_per_epoch: save_strategy: steps save_steps: 100 save_total_limit: 10 num_epochs: 1 #max_steps: 162945 # eval_table_size: # eval_max_new_tokens: 128 train_on_inputs: false group_by_length: false gradient_checkpointing: true early_stopping_patience: lr_scheduler: linear optimizer: adamw_bnb_8bit weight_decay: 0.01 xformers_attention: flash_attention: true resume_from_checkpoint: auto_resume_from_checkpoints: true local_rank: fsdp: fsdp_config: deepspeed: debug: strict: false # load_best_model_at_end: True max_grad_norm: 0.3 ```

# mistral-tenglish-april5_2 This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) 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: 2e-05 - train_batch_size: 7 - eval_batch_size: 7 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 56 - total_eval_batch_size: 28 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 50 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.0