model: task: text-generation system_prompt: "너는 주어진 Context에서 Question에 대한 Answer를 찾는 챗봇이야. Context에서 Answer가 될 수 있는 부분을 찾아서 그대로 적어줘. 단, Answer는 주관식이 아니라 단답형으로 적어야 해." path: MLP-KTLim/llama-3-Korean-Bllossom-8B torch_dtype: auto device_map: auto attn_implementation: sdpa dataset: path: jijihuny/economics_qa name: train shuffle: false test_size: null include_answer: true metric: path: jijihuny/ecqa generation: # 프롬프트를 포함하지 않음(false) return_full_text: false # 생성할 최대 토큰 숫자 max_new_tokens: null # Stochastic Decoding Algorithm do_sample: false # 상위 K개의 Vocab top_k: 1 # Smallest subset V' s.t \sum_{v \in V} v \geq p top_p: 0.95 # softmax(x/T) # T > 1 => smooth(uniform as T -> \infty) # 0 <= T < 1 => sharpen(deterministic as T -> 0+) temperature: 1.0 # penalty on generated token. temperature보다 높아야함 repetition_penalty: null # Contrastive search # Degeneration penalty # argmax (1-alpha) * p(v, x_{user<|end_header_id|>" response_template: "<|start_header_id|>assistant<|end_header_id|>" use_completion_only_data_collator: false quantization: load_in_4bit: true bnb_4bit_quant_type: nf4 bnb_4bit_compute_dtype: bfloat16 bnb_4bit_use_double_quant: true lora: r: 16 lora_alpha: 32 lora_dropout: 0.05 bias: none target_modules: - up_proj - down_proj - gate_proj - k_proj - q_proj - v_proj - o_proj # - lm_head task_type: CAUSAL_LM args: output_dir: llama3-qlora-r16-a32 run_name: llama3-qlora-r16-a32 report_to: wandb # dataloader_num_workers: 4 torch_empty_cache_steps: 3 # group_by_length: true max_seq_length: 2048 eval_strategy: steps per_device_train_batch_size: 16 per_device_eval_batch_size: 32 gradient_accumulation_steps: 1 eval_accumulation_steps: 1 optim: paged_adamw_8bit bf16: true bf16_full_eval: true learning_rate: 0.0002 weight_decay: 0.01 num_train_epochs: 3 warmup_ratio: 0.005 max_grad_norm: 2.0 eval_steps: 0.2 eval_on_start: false save_steps: 0.2 logging_steps: 1 push_to_hub: true # torch_compile: true seed: 42