--- library_name: peft tags: - generated_from_trainer datasets: - Guilherme34/uncensor base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B model-index: - name: outputs/out/DeepSeek-R1-Distill-Qwen-7B-Uncensored results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.6.0` ```yaml base_model: /cpool/DeepSeek-R1-Distill-Qwen-7B load_in_8bit: false load_in_4bit: false strict: false datasets: - path: Guilherme34/uncensor type: chat_template chat_template: llama3 field_messages: messages message_field_role: role message_field_content: content roles: system: - system user: - user assistant: - assistant dataset_prepared_path: last_run_prepared val_set_size: 0.0 output_dir: ./outputs/out/DeepSeek-R1-Distill-Qwen-7B-Uncensored save_safetensors: true sequence_len: 4096 sample_packing: false pad_to_sequence_len: true adapter: lora lora_model_dir: lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: gradient_accumulation_steps: 4 micro_batch_size: 1 num_epochs: 4 optimizer: adamw_torch lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: true tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: true logging_steps: 1 flash_attention: true warmup_steps: 10 evals_per_epoch: 1 eval_table_size: 20 eval_max_new_tokens: 128 saves_per_epoch: 1 save_total_limit: 20 debug: deepspeed: weight_decay: 0.0 fsdp: - full_shard - auto_wrap fsdp_config: fsdp_limit_all_gathers: true fsdp_sync_module_states: true fsdp_offload_params: true fsdp_use_orig_params: false fsdp_cpu_ram_efficient_loading: true fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP fsdp_transformer_layer_cls_to_wrap: Qwen2DecoderLayer fsdp_state_dict_type: FULL_STATE_DICT fsdp_sharding_strategy: FULL_SHARD special_tokens: ```

# outputs/out/DeepSeek-R1-Distill-Qwen-7B-Uncensored This model was trained from scratch on the Guilherme34/uncensor 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: 0.0002 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - total_eval_batch_size: 2 - 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: 10 - num_epochs: 4 ### Training results ### Framework versions - PEFT 0.14.0 - Transformers 4.47.1 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0