🧠 DeepSeek-R1-Distill-Llama-70B-Ko-Reasoning

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.

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📌 Overview

DeepSeek-R1-Distill-Llama-70B-Ko-Reasoning is a fine-tuned version of deepseek-ai/DeepSeek-R1-Distill-Llama-70B, specifically optimized for logical reasoning in Korean. This model is part of a broader research initiative to explore:

• The transition from multilingual reasoning LLMs to Korean-specialized reasoning models
• The enhancement of non-reasoning Korean language models into reasoning-capable variants
• The development of open-access models that rival proprietary alternatives in complex reasoning tasks

This model was fine-tuned using a large-scale Korean-English instruction dataset containing diverse multi-hop questions, symbolic logic tasks, and human-crafted reasoning steps.

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🧑‍💻 Usage

Install Transformers >= 4.50:

pip install -U transformers

Basic example:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "DimensionSTP/DeepSeek-R1-Distill-Llama-70B-Ko-Reasoning"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "서울과 부산 중 어디가 더 커?"
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

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🧠 Base Model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B

The base model, deepseek-ai/DeepSeek-R1-Distill-Llama-70B, is a CoT LLM developed by the DeepSeek AI team, fine tuned from Llama 3.3 instruct. For more technical details, refer to the Deepseek R1 Technical Report.

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🧱 Model Architecture

Property	Value
Architecture	LlamaForCausalLM
Parameters	70B
Context Length	131,072 tokens
Tokenizer	LLamaTokenizer (BPE)

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📅 Release Date

Mar 2025
This model was released in March 2025 as part of the Ko-Reasoning Series, which focuses on pushing the boundaries of open-source reasoning in Korean using modern LLMs.

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📬 Contact

For questions, collaborations, or deployment inquiries, please contact:

• 🤖 Hugging Face: https://huggingface.co/DimensionSTP
• ✉️ Email: [[email protected]]

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📦 Available Checkpoints

• ✅ main: Final stable version from the last branch
• ✅ All training artifacts available (tokenizer, config, model weights)