Model Details

Full Training Pipeline

SFT Epoch 3 Training Details

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

Chat Format (ChatML)

markdown
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<|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! 파이썬 리스트 정렬 방법을 알려주세요.<|im_end|>
<|im_start|>assistant

How to Use

With vLLM (recommended)

bash
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python -m vllm.entrypoints.openai.api_server \
    --model mkd-hossain/keural-sft3-50k \
    --dtype auto \
    --max-model-len 4096 \
    --gpu-memory-utilization 0.7

python
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from openai import OpenAI

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

response = client.chat.completions.create(
    model="mkd-hossain/keural-sft3-50k",
    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

python
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from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "mkd-hossain/keural-sft3-50k"
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."},
    {"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 — do not use ID 2.

Checkpoint Comparison

Limitations

• Maximum context is 4,096 tokens.
• This is an intermediate checkpoint — epoch 3 completes at step 65,849.
• Not safety-aligned — do not deploy in production without additional safety fine-tuning.
• DPO round 2 planned (485,793 pairs) after SFT epoch 3 completes.

License

Apache 2.0