285 lines
8.4 KiB
Markdown
285 lines
8.4 KiB
Markdown
---
|
||
language:
|
||
- ko
|
||
- en
|
||
license: apache-2.0
|
||
library_name: transformers
|
||
tags:
|
||
- mixtral
|
||
- moe
|
||
- korean
|
||
- bilingual
|
||
- causal-lm
|
||
- dpo
|
||
- rlhf
|
||
- instruction-tuned
|
||
- chat
|
||
base_model: mkd-hossain/keural-sft-18k
|
||
pipeline_tag: 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 + `<|im_start|>` + `<|im_end|>`) |
|
||
| 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`
|
||
|
||
```python
|
||
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)
|
||
|
||
```bash
|
||
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:
|
||
|
||
```python
|
||
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
|
||
|
||
```bash
|
||
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
|
||
|
||
```python
|
||
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|>` | 131072 | Marks the start of each conversation turn |
|
||
| `<|im_end|>` | 131073 | Marks end of turn / generation stop token |
|
||
| `<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
|
||
|
||
```python
|
||
# 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
|