--- library_name: transformers tags: - generated_from_trainer datasets: - json model-index: - name: raid/hoangpv4/models/specialized_llm_3b_qwen_base_2000 results: [] --- [Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config axolotl version: `0.6.0` ```yaml base_model: /raid/HUB_LLM/Qwen2.5-3B trust_remote_code: true load_in_8bit: false load_in_4bit: false strict: false chat_template: qwen_25 datasets: - path: json data_files: - /workspace/home/namb/hoangpv4/kg_fact_checking/data/train_specialized_llm/data_ready_to_train_2000.jsonl type: chat_template field_messages: messages message_field_role: role message_field_content: content val_set_size: 0.0 output_dir: /raid/hoangpv4/models/specialized_llm_3b_qwen_base_2000 sequence_len: 256 sample_packing: false pad_to_sequence_len: true wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 1 micro_batch_size: 8 num_epochs: 1 optimizer: adamw_torch lr_scheduler: constant learning_rate: 2e-5 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: true gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: false early_stopping_patience: resume_from_checkpoint: logging_steps: 5 xformers_attention: flash_attention: true warmup_steps: 100 evals_per_epoch: 2 eval_table_size: saves_per_epoch: 1 debug: deepspeed: /workspace/home/namb/hoangpv4/kg_fact_checking/axolotl_config/zero3.json weight_decay: 0.0 fsdp: fsdp_config: special_tokens: eos_token: <|im_end|> tokens: - "" - "" - "~" ```

# raid/hoangpv4/models/specialized_llm_3b_qwen_base_2000 This model was trained from scratch on the json 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: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 16 - total_eval_batch_size: 16 - 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: constant - lr_scheduler_warmup_steps: 100 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.47.1 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0