--- base_model: mayflowergmbh/occiglot-10b-de-en-instruct tags: - generated_from_trainer model-index: - name: occiglot10b/ results: [] --- [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl)
See axolotl config axolotl version: `0.4.0` ```yaml base_model: mayflowergmbh/occiglot-10b-de-en-instruct model_type: MistralForCausalLM tokenizer_type: LlamaTokenizer is_mistral_derived_model: true load_in_8bit: false load_in_4bit: true strict: false rl: dpo datasets: - path: johannhartmann/mistralorpo split: train type: chatml.intel dataset_prepared_path: last_run_prepared val_set_size: 0.0 output_dir: ./occiglot10b/ save_total_limit: 3 adapter: qlora lora_model_dir: sequence_len: 2048 sample_packing: false pad_to_sequence_len: false lora_r: 64 lora_alpha: 32 lora_dropout: 0.05 lora_target_linear: true lora_modules_to_save: - embed_tokens - lm_head lora_fan_in_fan_out: lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj wandb_project: occiglot wandb_entity: mayflowerteam wandb_watch: wandb_run_id: wandb_log_model: gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 1 optimizer: paged_adamw_8bit adam_beta2: 0.95 adam_epsilion: 0.00001 lr_scheduler: cosine learning_rate: 2e-5 train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true warmup_steps: 100 eval_steps: eval_table_size: eval_table_max_new_tokens: 128 save_steps: 239 debug: deepspeed: weight_decay: 0.1 fsdp: fsdp_config: special_tokens: save_safetensors: true special_tokens: eos_token: "<|im_end|>" tokens: # these are delimiters - "<|im_start|>" chat_template: chatml ```

# occiglot10b/ This model is a fine-tuned version of [mayflowergmbh/occiglot-10b-de-en-instruct](https://huggingface.co./mayflowergmbh/occiglot-10b-de-en-instruct) on an unknown 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - training_steps: 2698 ### Training results ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.1.2+cu118 - Datasets 2.18.0 - Tokenizers 0.15.0