keural-sft3-40k

Keural-SFT3-14.83B (SFT Epoch 3 — 40,000 steps)

Keural is a bilingual Korean–English Mixture-of-Experts language model trained entirely from scratch — no base model was used. This is an intermediate SFT epoch 3 checkpoint at step 40,000 out of 65,849 total steps, trained on a 2.35M sample merged dataset.

Model Details

Full Training Pipeline

SFT Epoch 3 Training Details

SFT Epoch 3 Dataset (2,351,212 samples)

Chat Format (ChatML)

This model uses ChatML format with automatic system prompt injection.

<|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

How to Use

With vLLM (recommended)

pip install vllm

python -m vllm.entrypoints.openai.api_server \
    --model mkd-hossain/keural-sft3-40k \
    --dtype auto \
    --max-model-len 4096 \
    --gpu-memory-utilization 0.7

from openai import OpenAI

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

response = client.chat.completions.create(
    model="mkd-hossain/keural-sft3-40k",
    messages=[
        {"role": "system", "content": "You are a helpful bilingual Korean-English assistant. Always respond in the same language as the user."},
        {"role": "user", "content": "인공지능이란 무엇인가요?"},
    ],
    max_tokens=512,
    temperature=0.7,
)
print(response.choices[0].message.content)

With transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "mkd-hossain/keural-sft3-40k"

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."},
    {"role": "user", "content": "파이썬 리스트 정렬 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,
        repetition_penalty=1.1,
        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)

Special Tokens

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

Checkpoint Comparison

Limitations

• Maximum context is 4,096 tokens.
• This is an intermediate checkpoint — epoch 3 is still running (40K/65,849 steps).
• Not safety-aligned — do not deploy in production without additional safety fine-tuning.
• English factual knowledge is limited due to pretraining data scale (63B tokens vs 15T for larger models).
• DPO round 2 planned after SFT epoch 3 completes to improve factual accuracy and reduce hallucination.

License

Apache 2.0