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Model: mkd-hossain/keural-dpo-final Source: Original Platform
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README.md
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README.md
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---
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language:
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- ko
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- en
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license: apache-2.0
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library_name: transformers
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tags:
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- mixtral
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- moe
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- korean
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- bilingual
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- causal-lm
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- dpo
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- rlhf
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- instruction-tuned
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- chat
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base_model: mkd-hossain/keural-sft-18k
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pipeline_tag: text-generation
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---
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# Keural-DPO-14.83B (Final — 6927 steps, 1 full epoch)
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Keural is a bilingual Korean–English Mixture-of-Experts language model trained **entirely from scratch** — no base model was used.
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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.
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> 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.
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## Model Details
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| Property | Value |
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|---|---|
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| Architecture | Mixtral-style MoE (8 experts, top-2 routing) |
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| Parameters | **14.83B total** / ~7.42B active per token |
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| Layers | 24 |
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| Hidden size | 4096 |
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| Attention heads | 32 (GQA — 8 KV heads) |
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| Head dim | 128 |
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| Expert intermediate size | 5,632 |
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| Experts | 8 total, top-2 per token |
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| Context length | 4,096 tokens |
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| Vocabulary | 131,074 (131,072 SPM + `<|im_start|>` + `<|im_end|>`) |
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| RoPE theta | 500,000 |
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| Sliding window | 512 (alternating every other layer) |
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| Norm | RMSNorm (eps=1e-5) |
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| Activation | SiLU |
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| Dtype | bfloat16 |
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| Languages | Korean (primary), English |
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## Full Training Pipeline
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| Stage | Steps | Tokens | Data | Hardware |
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|---|---|---|---|---|
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| Pretraining Stage 1 | 100,000 | ~50B | Korean + English web corpus | 2× H200 SXM |
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| Pretraining Stage 2 | 120,000 | ~13B | Korean + English web corpus (continued) | 2× H200 SXM |
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| SFT | 18,000 | 710M | mkd-chanwoo/keural-SFT (1.14M ChatML samples) | 2× H200 SXM |
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| **DPO (this checkpoint)** | **6,927 (1 full epoch)** | — | keural-dpo-raw (440K preference pairs) | 2× H200 SXM |
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### DPO Training Details
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| Hyperparameter | Value |
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|---|---|
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| Algorithm | Direct Preference Optimization (DPO) |
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| Learning rate | 2e-6 → 2e-7 cosine decay |
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| Min learning rate | 2e-7 |
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| Warmup steps | 100 |
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| Beta (KL penalty) | 0.1 |
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| Batch size per GPU | 2 |
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| Gradient accumulation | 16 steps |
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| Effective batch size | 64 (2 × 16 × 2 GPUs) |
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| Max sequence length | 1,024 tokens |
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| Optimizer | AdamW (β1=0.9, β2=0.95, ε=1e-8) |
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| Weight decay | 0.1 |
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| Gradient clipping | 1.0 |
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| Total steps | 6,927 (1 full epoch) |
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| Dataset size | 440,627 preference pairs |
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| Parallelism | FSDP FULL_SHARD (ZeRO-3 equivalent) |
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| Precision | bfloat16 + gradient checkpointing |
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| Hardware | 2× NVIDIA H200 SXM (139 GiB each) |
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| Speed | ~40 seconds/step |
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| Final loss | ~0.6924 (stable) |
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| Final margin | +0.0009–0.0018 (consistently positive) |
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| Final GradNorm | 0.20–0.31 (clean) |
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### DPO Dataset Sources
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| Source | Samples | Language |
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|---|---|---|
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| hh_rlhf | 159,777 | English |
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| aihub_71760 | 116,320 | Korean |
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| multifaceted_collection_dpo | 63,399 | English |
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| ultrafeedback_binarized | 59,122 | English |
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| aihub_71748 | 29,676 | Korean |
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| orca_dpo_paris_ko | 12,714 | Korean |
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| **Total** | **440,627** | |
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### SFT Hyperparameters (base checkpoint)
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| Hyperparameter | Value |
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|---|---|
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| Learning rate | 1e-5 → 1e-6 cosine decay |
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| Effective batch size | 64 (4 per GPU × 8 grad accum × 2 GPUs) |
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| Max sequence length | 4,096 tokens |
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| Weight decay | 0.05 |
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| Steps | 18,000 |
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| Dataset | mkd-chanwoo/keural-SFT (1.14M samples) |
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## Chat Format (ChatML)
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This model uses **ChatML** format. Always include a system prompt for best results.
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```
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<|im_start|>system
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You are a helpful bilingual Korean-English assistant. Always respond in the same language as the user.<|im_end|>
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<|im_start|>user
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안녕하세요! 오늘 날씨가 어때요?<|im_end|>
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<|im_start|>assistant
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```
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The model generates until it produces `<|im_end|>` (token ID 131073).
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> 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`.
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## How to Use
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### With `transformers`
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "mkd-hossain/keural-dpo-final"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{
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"role": "system",
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"content": (
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"You are a helpful bilingual Korean-English assistant. "
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"Always respond in the same language as the user's message."
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)
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},
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{"role": "user", "content": "파이썬에서 리스트를 정렬하는 방법을 알려주세요."},
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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with torch.no_grad():
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output = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.9,
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top_k=50,
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repetition_penalty=1.1,
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no_repeat_ngram_size=8,
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do_sample=True,
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eos_token_id=131073,
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)
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response = tokenizer.decode(output[0][inputs.input_ids.shape[1]:], skip_special_tokens=False)
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response = response.split("<|im_end|>")[0].strip()
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print(response)
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```
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### With vLLM (recommended for serving)
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```bash
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pip install vllm
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python -m vllm.entrypoints.openai.api_server \
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--model mkd-hossain/keural-dpo-final \
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--tokenizer mkd-hossain/keural-dpo-final \
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--dtype bfloat16 \
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--max-model-len 4096 \
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--tensor-parallel-size 1
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```
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Call the OpenAI-compatible endpoint:
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```python
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from openai import OpenAI
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client = OpenAI(base_url="http://localhost:8000/v1", api_key="none")
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response = client.chat.completions.create(
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model="mkd-hossain/keural-dpo-final",
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messages=[
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{"role": "system", "content": "You are a helpful bilingual assistant. Respond in the same language as the user."},
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{"role": "user", "content": "What is the capital of South Korea?"},
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],
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max_tokens=512,
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temperature=0.7,
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)
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print(response.choices[0].message.content)
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```
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### Multi-GPU serving
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```bash
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python -m vllm.entrypoints.openai.api_server \
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--model mkd-hossain/keural-dpo-final \
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--dtype bfloat16 \
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--max-model-len 4096 \
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--tensor-parallel-size 2
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```
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### Manual ChatML prompt
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```python
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prompt = (
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"<|im_start|>system\n"
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"You are a helpful bilingual Korean-English assistant. "
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"Always respond in the same language as the user.\n"
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"<|im_end|>\n"
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"<|im_start|>user\n"
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"Tell me about Seoul.<|im_end|>\n"
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"<|im_start|>assistant\n"
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)
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```
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## Special Tokens
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| Token | ID | Purpose |
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| `<|im_start|>` | 131072 | Marks the start of each conversation turn |
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| `<|im_end|>` | 131073 | Marks end of turn / generation stop token |
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| `<bos>` | 1 | Beginning of sequence |
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| `<eos>` | 2 | End of sequence (not used for chat) |
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| `<pad>` | 0 | Padding token |
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> **Critical:** Always set `eos_token_id=131073` when generating. Do **not** use `eos_token_id=2`.
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## Recommended Generation Settings
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```python
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# Conversational / creative
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{
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"max_new_tokens": 512,
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"temperature": 0.7,
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"top_p": 0.9,
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"top_k": 50,
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"repetition_penalty": 1.1,
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"no_repeat_ngram_size": 8,
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"do_sample": True,
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"eos_token_id": 131073,
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}
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# Factual / deterministic
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{
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"max_new_tokens": 512,
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"temperature": 0.1,
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"repetition_penalty": 1.1,
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"do_sample": False,
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"eos_token_id": 131073,
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}
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```
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## Checkpoint Comparison
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| Checkpoint | Stage | Steps | Notes |
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| mkd-hossain/keural-pretrained | Pretraining | 120,000 | Raw base, no instruction tuning |
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| mkd-hossain/keural-sft-18k | SFT | 18,000 | Instruction following, ChatML format |
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| mkd-hossain/keural-dpo-3500 | DPO 50% | 3,500 | Early alignment |
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| mkd-hossain/keural-dpo-5500 | DPO 79% | 5,500 | Late alignment |
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| **mkd-hossain/keural-dpo-final** | **DPO 100%** | **6,927** | **Full epoch — best checkpoint** |
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## Limitations
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- Maximum context is 4,096 tokens.
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- The pretraining corpus is Korean-dominant — always include a system prompt for correct bilingual behavior.
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- Not safety-aligned — do not deploy in production without additional safety fine-tuning.
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- This is an intermediate model in an ongoing training pipeline. Future releases will include SFT epoch 2 on filtered data and DPO round 2.
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## License
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Apache 2.0
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