--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - axolotl - generated_from_trainer model-index: - name: Mistral-7B-Instruct-KhanAcademy-v0.2 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-Instruct-v0.2 model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer is_mistral_derived_model: true hub_model_id: MaziyarPanahi/Mistral-7B-Instruct-KhanAcademy-v0.2 hf_use_auth_token: true load_in_8bit: false load_in_4bit: false strict: false datasets: - path: CohereForAI/aya_dataset type: system_prompt: "" field_instruction: inputs field_output: targets format: "[INST] {instruction} [/INST]" no_input_format: "[INST] {instruction} [/INST]" dataset_prepared_path: val_set_size: 0.05 output_dir: ./models/MaziyarPanahi/Mistral-7B-Instruct-Aya-101 sequence_len: 8192 sample_packing: true pad_to_sequence_len: true eval_sample_packing: false wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 1 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.000005 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: bos_token: "" eos_token: "" unk_token: "" ```

# Mistral-7B-Instruct-KhanAcademy-v0.2 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.1502 ## 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: 5e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.9039 | 0.0 | 1 | 3.1495 | | 0.9933 | 0.25 | 101 | 1.2402 | | 0.9439 | 0.5 | 202 | 1.1683 | | 0.9762 | 0.75 | 303 | 1.1502 | ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.0