license: apache-2.0
language:
- ko
polyglot-ko-12.8b-instruct
This model is a fine-tuned version of EleutherAI/polyglot-ko-12.8b on an instruction-following dataset(260k).
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- seed: 42
- distributed_type: multi-GPU(A100 80G)
- num_devices: 8
- gradient_accumulation_steps: 64
- total_train_batch_size: 512
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
Inference
'''python import torch from transformers import pipeline, AutoModelForCausalLM
MODEL = 'etri-xainlp/polyglot-ko-12.8b-instruct'
model = AutoModelForCausalLM.from_pretrained( MODEL, torch_dtype=torch.float16, low_cpu_mem_usage=True, ).to(device=7, non_blocking=True) model.eval()
pipe = pipeline( 'text-generation', model=model, tokenizer=MODEL, device=7 ) pipe.model.config.pad_token_id = pipe.model.config.eos_token_id
def ask(x, context='', is_input_full=False): ans = pipe( f"### 질문: {x}\n\n### 맥락: {context}\n\n### 답변:" if context else f"### 질문: {x}\n\n### 답변:", do_sample=True, max_new_tokens=2048, temperature=0.9, top_p=0.9, return_full_text=False, eos_token_id=2, ) return ans[0]['generated_text']
while True: quit = input('prompt?: ') if quit == 'q': break else: generation = ask(quit) print("etri_ai:", generation) ...
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3