Instructions to use mkd-hossain/Keural-Cortex-8B-step5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mkd-hossain/Keural-Cortex-8B-step5000 with Transformers:

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

pipe = pipeline("text-generation", model="mkd-hossain/Keural-Cortex-8B-step5000")
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-step5000")
model = AutoModelForCausalLM.from_pretrained("mkd-hossain/Keural-Cortex-8B-step5000", 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-step5000 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-step5000"
# 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-step5000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mkd-hossain/Keural-Cortex-8B-step5000

SGLang

How to use mkd-hossain/Keural-Cortex-8B-step5000 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-step5000" \
    --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-step5000",
		"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-step5000" \
        --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-step5000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mkd-hossain/Keural-Cortex-8B-step5000 with Docker Model Runner:
```
docker model run hf.co/mkd-hossain/Keural-Cortex-8B-step5000
```

Keural Cortex 8B — intermediate checkpoint (step 5000)

This is a mid-training checkpoint, not a finished model. It is published for internal inspection and reproducibility, not for use.

Continued pretraining (CPT) of Qwen/Qwen3-8B-Base on a Korean-focused corpus, by MKD Co., Ltd.

Status


Training progress	step 5,000 / 13,034 (~38%)
Tokens seen	~15.7B of 41B planned
Stage	Phase 4 (main CPT) — in progress
Context length	32,768 (native, unchanged from base)

What this model cannot do

It is a base model. Qwen3-8B-Base has no instruction following, no chat format, no tool calling and no thinking mode, and CPT does not add any of them — those are built in post-training, which has not been done yet.

In practice it continues text rather than answering instructions. It will ramble, drift off topic, or repeat. A chat_template.jinja is present because it is inherited from the base tokenizer; it does not mean the model is chat-tuned.

Training

4× H200, FSDP full shard, bf16 with fp32 gradient reduction
seq_len 4,096, global batch 3,145,728 tokens/step
Peak LR 1e-5, WSD schedule, grad clip 1.0
Corpus: 38.78B tokens across 19 sources (Korean, English, code, science/math), license-audited for commercial use

Evaluation

No evaluation has been run on this checkpoint. The numbers below are from the 3B-token pilot (a separate, earlier run) against Qwen3-8B-Base, and are included only to show the recipe was validated before the main run:

Track	Mean delta vs base
Korean (KMMLU, HaeRae, KoBEST)	+0.55 pt
English (MMLU, ARC, HellaSwag, WinoGrande)	+0.62 pt

Caveat worth stating plainly: in that pilot, KMMLU (+0.03) and HaeRae (+0.09) were essentially flat, and the Korean average was carried by KoBEST BoolQ (+5.91) while two KoBEST tasks regressed. The Korean gain magnitude for the full run is not yet established.

Benchmark decontamination (n-gram overlap against MMLU, GSM8K, KMMLU, HaeRae, KoBEST and others) has not yet been completed. No benchmark number from this project should be treated as publishable until it has.

License and attribution

Apache 2.0, inherited from Qwen/Qwen3-8B-Base. This is a derivative work of Qwen3-8B-Base by Alibaba Cloud.

Intended use

Internal research and checkpoint inspection only. Not for production, not for public-facing deployment, and not a basis for capability claims.

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Safetensors

Model size

2B params

Tensor type

BF16

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Qwen/Qwen3-8B-Base

Finetuned

(661)

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