Instructions to use mkd-hossain/Keural-Cortex-8B-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mkd-hossain/Keural-Cortex-8B-DPO with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mkd-hossain/Keural-Cortex-8B-DPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mkd-hossain/Keural-Cortex-8B-DPO")
model = AutoModelForCausalLM.from_pretrained("mkd-hossain/Keural-Cortex-8B-DPO", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mkd-hossain/Keural-Cortex-8B-DPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mkd-hossain/Keural-Cortex-8B-DPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mkd-hossain/Keural-Cortex-8B-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mkd-hossain/Keural-Cortex-8B-DPO

SGLang

How to use mkd-hossain/Keural-Cortex-8B-DPO with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mkd-hossain/Keural-Cortex-8B-DPO" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mkd-hossain/Keural-Cortex-8B-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mkd-hossain/Keural-Cortex-8B-DPO" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mkd-hossain/Keural-Cortex-8B-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mkd-hossain/Keural-Cortex-8B-DPO with Docker Model Runner:
```
docker model run hf.co/mkd-hossain/Keural-Cortex-8B-DPO
```

Keural-Cortex-8B-DPO

Keural-Cortex-8B-DPO is a Korean-strong, bilingual, text-only assistant derived from Qwen/Qwen3-8B-Base. It preserves the Qwen3 architecture and tokenizer, uses a 65,536-token YaRN context configuration, and has been post-trained for instruction following, tool calling, reasoning, and Keural/MKD identity.

Release status

This is the completed DPO-v1 checkpoint: 4,473 steps trained on 291,084 preference pairs.

The post-DPO benchmark battery is not complete. This card reports only verified serving behavior and clearly labels predecessor measurements; it does not claim that DPO has improved any benchmark until that evaluation is run.

Training lineage

Qwen/Qwen3-8B-Base
  -> 41.00B-token continued pretraining
  -> 64K context extension (YaRN)
  -> SFT v2
  -> DPO v1, step 4,473 (this release)

This is a derivative of Qwen3-8B-Base, not a model trained from scratch.

Architecture

Property	Value
Architecture	Qwen3ForCausalLM, dense decoder-only
Parameters	8.19B
Context	65,536 tokens
Position encoding	YaRN, factor 2.0
Precision	bfloat16
Model type	`qwen3`
Primary languages	Korean and English

What was verified

Serving and thinking routing

The included chat_template.jinja is an inference-only fix for this DPO checkpoint. It opens the reasoning block with a short seed line, which helps the model finish the block with </think>.

With the vLLM command below and enable_thinking: true, the server was verified to return separate reasoning and content fields. This validates response routing and format, not reasoning accuracy.

Important quality caveat

A single factory-rate test produced correctly separated reasoning and answer fields but an incorrect answer (1 minute instead of 5 minutes). Do not infer mathematical reliability from the presence of a reasoning block. Full post-DPO evaluation remains required.

Pre-DPO evidence

The SFT-v2 predecessor passed tests for single and parallel tool calls, tool-result loops, multi-turn memory, retrieval through 55K context, and non-thinking chat. DPO targeted thinking-block routing, dependent sequential tool chains, and identity consistency. These targets must be re-evaluated on this checkpoint.

vLLM: tested configuration

Install a current vLLM release in a separate inference environment; do not replace the PyTorch installation used for training.

vllm serve mkd-hossain/Keural-Cortex-8B-DPO \
  --max-model-len 65536 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes

For a local copy of this checkpoint, explicitly use the included template:

vllm serve /path/to/step_0004473 \
  --chat-template /path/to/chat_template.jinja \
  --max-model-len 65536 \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes

Parser choice matters:

qwen3 parses <think>...</think> into vLLM's separate reasoning field.
hermes parses this release's JSON <tool_call> format.
Do not use vLLM's newer qwen3 tool-call parser for this checkpoint: it expects a different XML function-call format.

API examples

Thinking mode

Use sampling for reasoning requests; avoid greedy decoding.

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mkd-hossain/Keural-Cortex-8B-DPO",
    "messages": [{"role": "user", "content": "Solve this carefully: if 100 machines each make 5 widgets in 5 minutes, how long do they take to make 100 widgets?"}],
    "temperature": 0.6,
    "top_p": 0.95,
    "chat_template_kwargs": {"enable_thinking": true}
  }'

The response should contain message.reasoning and message.content separately.

Normal fast response

"chat_template_kwargs": {"enable_thinking": false}

Tool calling

Start vLLM with the flags above, pass OpenAI-format tool definitions, and set tool_choice to auto. Validate all model-supplied tool arguments before executing a tool.

Limitations

No validated automatic thinking selector exists. Use enable_thinking: true for complex math, code, planning, and multi-step tool tasks; use false for ordinary chat.
DPO benchmark results are pending. Do not make external performance claims from prompts or the predecessor evaluation.
Long-context retrieval was measured before DPO; long-context reasoning and instruction following are less established.
The model can hallucinate, make arithmetic errors, misuse tools, or generate unsafe content. It is not a substitute for professional judgement.

License and attribution

The base model is Qwen/Qwen3-8B-Base under Apache-2.0. This derivative release is provided under Apache-2.0, subject to the provenance and dataset obligations documented by the project.

Citation

@software{keural_cortex_8b_dpo_2026,
  title = {Keural-Cortex-8B-DPO},
  author = {MKD Co., Ltd.},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/mkd-hossain/Keural-Cortex-8B-DPO}
}

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Safetensors

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2B params

Tensor type

BF16

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