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A large-scale Korean reasoning model fine-tuned from deepseek-ai/DeepSeek-R1-Distill-Llama-70B, designed to excel in logical and multi-hop reasoning tasks in Korean.
pip install -U transformers1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model_name = "DimensionSTP/DeepSeek-R1-Distill-Llama-70B-Ko-Reasoning"
4
5model = AutoModelForCausalLM.from_pretrained(
6 model_name,
7 torch_dtype="auto",
8 device_map="auto"
9)
10tokenizer = AutoTokenizer.from_pretrained(model_name)
11
12prompt = "서울과 부산 중 어디가 더 커?"
13messages = [
14 {"role": "user", "content": prompt}
15]
16text = tokenizer.apply_chat_template(
17 messages,
18 tokenize=False,
19 add_generation_prompt=True
20)
21
22model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
23
24generated_ids = model.generate(
25 **model_inputs,
26 max_new_tokens=32768
27)
28generated_ids = [
29 output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
30]
31
32response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
33print(response)| Property | Value |
|---|---|
| Architecture | LlamaForCausalLM |
| Parameters | 70B |
| Context Length | 131,072 tokens |
| Tokenizer | LLamaTokenizer (BPE) |
main: Final stable version from the last branch