language, license, library_name, tags, base_model, pipeline_tag

language	license	library_name	tags	base_model	pipeline_tag
ko en	apache-2.0	transformers	mixtral moe korean bilingual causal-lm dpo rlhf instruction-tuned chat	mkd-hossain/keural-sft-18k	text-generation

Keural-DPO-14.83B (Final — 6927 steps, 1 full epoch)

Keural is a bilingual Korean–English Mixture-of-Experts language model trained entirely from scratch — no base model was used. This is the final DPO (Direct Preference Optimization) checkpoint at step 6,927, completing 1 full epoch of preference alignment from the Keural SFT-18k base.

This is the most capable Keural checkpoint released to date. One full epoch of DPO alignment on 440K Korean+English preference pairs, producing consistently positive reward margins throughout training.

Model Details

Property	Value
Architecture	Mixtral-style MoE (8 experts, top-2 routing)
Parameters	14.83B total / ~7.42B active per token
Layers	24
Hidden size	4096
Attention heads	32 (GQA — 8 KV heads)
Head dim	128
Expert intermediate size	5,632
Experts	8 total, top-2 per token
Context length	4,096 tokens
Vocabulary	131,074 (131,072 SPM + `<
RoPE theta	500,000
Sliding window	512 (alternating every other layer)
Norm	RMSNorm (eps=1e-5)
Activation	SiLU
Dtype	bfloat16
Languages	Korean (primary), English

Full Training Pipeline

Stage	Steps	Tokens	Data	Hardware
Pretraining Stage 1	100,000	~50B	Korean + English web corpus	2× H200 SXM
Pretraining Stage 2	120,000	~13B	Korean + English web corpus (continued)	2× H200 SXM
SFT	18,000	710M	mkd-chanwoo/keural-SFT (1.14M ChatML samples)	2× H200 SXM
DPO (this checkpoint)	6,927 (1 full epoch)	—	keural-dpo-raw (440K preference pairs)	2× H200 SXM

DPO Training Details

Hyperparameter	Value
Algorithm	Direct Preference Optimization (DPO)
Learning rate	2e-6 → 2e-7 cosine decay
Min learning rate	2e-7
Warmup steps	100
Beta (KL penalty)	0.1
Batch size per GPU	2
Gradient accumulation	16 steps
Effective batch size	64 (2 × 16 × 2 GPUs)
Max sequence length	1,024 tokens
Optimizer	AdamW (β1=0.9, β2=0.95, ε=1e-8)
Weight decay	0.1
Gradient clipping	1.0
Total steps	6,927 (1 full epoch)
Dataset size	440,627 preference pairs
Parallelism	FSDP FULL_SHARD (ZeRO-3 equivalent)
Precision	bfloat16 + gradient checkpointing
Hardware	2× NVIDIA H200 SXM (139 GiB each)
Speed	~40 seconds/step
Final loss	~0.6924 (stable)
Final margin	+0.0009–0.0018 (consistently positive)
Final GradNorm	0.20–0.31 (clean)

DPO Dataset Sources

Source	Samples	Language
hh_rlhf	159,777	English
aihub_71760	116,320	Korean
multifaceted_collection_dpo	63,399	English
ultrafeedback_binarized	59,122	English
aihub_71748	29,676	Korean
orca_dpo_paris_ko	12,714	Korean
Total	440,627

SFT Hyperparameters (base checkpoint)

Hyperparameter	Value
Learning rate	1e-5 → 1e-6 cosine decay
Effective batch size	64 (4 per GPU × 8 grad accum × 2 GPUs)
Max sequence length	4,096 tokens
Weight decay	0.05
Steps	18,000
Dataset	mkd-chanwoo/keural-SFT (1.14M samples)

Chat Format (ChatML)

This model uses ChatML format. Always include a system prompt for best results.

<|im_start|>system
You are a helpful bilingual Korean-English assistant. Always respond in the same language as the user.<|im_end|>
<|im_start|>user
안녕하세요! 오늘 날씨가 어때요?<|im_end|>
<|im_start|>assistant

The model generates until it produces <|im_end|> (token ID 131073).

The chat template in tokenizer_config.json automatically injects a default system prompt if you don't provide one, so bilingual behavior works out of the box with apply_chat_template.

How to Use

With transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "mkd-hossain/keural-dpo-final"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {
        "role": "system",
        "content": (
            "You are a helpful bilingual Korean-English assistant. "
            "Always respond in the same language as the user's message."
        )
    },
    {"role": "user", "content": "파이썬에서 리스트를 정렬하는 방법을 알려주세요."},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.7,
        top_p=0.9,
        top_k=50,
        repetition_penalty=1.1,
        no_repeat_ngram_size=8,
        do_sample=True,
        eos_token_id=131073,
    )

response = tokenizer.decode(output[0][inputs.input_ids.shape[1]:], skip_special_tokens=False)
response = response.split("<|im_end|>")[0].strip()
print(response)

With vLLM (recommended for serving)

pip install vllm

python -m vllm.entrypoints.openai.api_server \
    --model mkd-hossain/keural-dpo-final \
    --tokenizer mkd-hossain/keural-dpo-final \
    --dtype bfloat16 \
    --max-model-len 4096 \
    --tensor-parallel-size 1

Call the OpenAI-compatible endpoint:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="none")

response = client.chat.completions.create(
    model="mkd-hossain/keural-dpo-final",
    messages=[
        {"role": "system", "content": "You are a helpful bilingual assistant. Respond in the same language as the user."},
        {"role": "user", "content": "What is the capital of South Korea?"},
    ],
    max_tokens=512,
    temperature=0.7,
)
print(response.choices[0].message.content)

Multi-GPU serving

python -m vllm.entrypoints.openai.api_server \
    --model mkd-hossain/keural-dpo-final \
    --dtype bfloat16 \
    --max-model-len 4096 \
    --tensor-parallel-size 2

Manual ChatML prompt

prompt = (
    "<|im_start|>system\n"
    "You are a helpful bilingual Korean-English assistant. "
    "Always respond in the same language as the user.\n"
    "<|im_end|>\n"
    "<|im_start|>user\n"
    "Tell me about Seoul.<|im_end|>\n"
    "<|im_start|>assistant\n"
)

Special Tokens

Token	ID	Purpose
`<	im_start	>`
`<	im_end	>`
<bos>	1	Beginning of sequence
<eos>	2	End of sequence (not used for chat)
<pad>	0	Padding token

Critical: Always set eos_token_id=131073 when generating. Do not use eos_token_id=2.

Recommended Generation Settings

# Conversational / creative
{
    "max_new_tokens": 512,
    "temperature": 0.7,
    "top_p": 0.9,
    "top_k": 50,
    "repetition_penalty": 1.1,
    "no_repeat_ngram_size": 8,
    "do_sample": True,
    "eos_token_id": 131073,
}

# Factual / deterministic
{
    "max_new_tokens": 512,
    "temperature": 0.1,
    "repetition_penalty": 1.1,
    "do_sample": False,
    "eos_token_id": 131073,
}

Checkpoint Comparison

Checkpoint	Stage	Steps	Notes
mkd-hossain/keural-pretrained	Pretraining	120,000	Raw base, no instruction tuning
mkd-hossain/keural-sft-18k	SFT	18,000	Instruction following, ChatML format
mkd-hossain/keural-dpo-3500	DPO 50%	3,500	Early alignment
mkd-hossain/keural-dpo-5500	DPO 79%	5,500	Late alignment
mkd-hossain/keural-dpo-final	DPO 100%	6,927	Full epoch — best checkpoint

Limitations

• Maximum context is 4,096 tokens.
• The pretraining corpus is Korean-dominant — always include a system prompt for correct bilingual behavior.
• Not safety-aligned — do not deploy in production without additional safety fine-tuning.
• This is an intermediate model in an ongoing training pipeline. Future releases will include SFT epoch 2 on filtered data and DPO round 2.

License

Apache 2.0