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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mkd-hossain/Keural-Cortex-8B-Base")
model = AutoModelForCausalLM.from_pretrained("mkd-hossain/Keural-Cortex-8B-Base", 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=40)
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-Base 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-Base"
# 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-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Docker Model Runner
How to use mkd-hossain/Keural-Cortex-8B-Base with Docker Model Runner:
```
docker model run hf.co/mkd-hossain/Keural-Cortex-8B-Base
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Keural Cortex 8B — Base (final CPT checkpoint, 41B tokens)

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

This is the completed main CPT run — the Phase 4 deliverable, 13,034 of 13,034 steps. It is a base model, not an instruction-tuned assistant.


Parameters	8.19 B
Tokens of continued pretraining	41,001,418,752 (41.00 B)
Steps	13,034 / 13,034 — complete
Context length	32,768 (native, unchanged from base)
Precision	bfloat16
Completed	2026-09-09

⚠️ Read this before using the model

1. It is a base model. Qwen3-8B-Base has no instruction following, no chat format, no tool calling and no thinking mode, and continued pretraining adds none of them. This model continues text; it does not reliably answer instructions. Post-training (SFT/DPO) has not been performed.

A chat_template.jinja is present because it is inherited from the base tokenizer — it does not mean the model is chat-tuned. If you drive the model through that template it will imitate assistant phrasing ("As an AI language model, I cannot…"), because Qwen3-8B-Base absorbed large quantities of assistant-style text during its 36 T-token pretraining. That is imitation, not instruction tuning, and it is easy to mistake for the real thing.

2. This checkpoint has not been evaluated. No benchmark numbers exist for it yet. The only evaluation in this project is of a separate 3 B-token pilot run (see below). Do not assume the pilot's results transfer.

3. Benchmark decontamination has not been run. N-gram overlap against MMLU, GSM8K, KMMLU, HAE-RAE, KoBEST and others has not been checked. Treat any benchmark number produced from this model as provisional until it is.

Training

Parameter	Value
Sequence length	4,096
Global batch	3,145,728 tokens/step (4 micro × 4,096 × 4 GPU × 48 accum)
Peak learning rate	1.0e-05
Schedule	WSD — 1% warmup / 89% stable / 10% decay, min ratio 0.1
Optimizer	AdamW, β=(0.9, 0.95), ε=1e-8, weight decay 0.1
Gradient clipping	1.0 (never triggered — max observed norm 0.48)
Parallelism	FSDP full shard, 4 × NVIDIA H200
Precision	bf16 parameters, fp32 gradient reduction
Attention	FlashAttention-2
Activation checkpointing	enabled
Wall clock	18 days 10 hours — single run, zero restarts
Throughput	25,766 tok/s (stdev 127, i.e. 0.5%)
MFU / HFU	32.0% / 42.7%

The learning rate is deliberately 1e-5 rather than a pretraining-scale 1e-4–3e-4: CPT starts from a converged model, and a pretraining LR destroys existing capability before teaching anything new. Evidence this worked: gradient norm never exceeded 0.48 against a 1.0 clip threshold, no loss spikes across 13,034 steps, and the pilot's English track improved rather than regressed.

Training loss

1.890 (step 20) → 1.819 (final). Loss plateaued at ≈1.818 from roughly step 5,200 (16.4 B tokens) and did not improve materially over the remaining ~25 B tokens; the WSD decay phase contributed +0.0007. Interpretation is open — corpus saturation, a conservatively low LR, or both — and is untested because intermediate checkpoints have not been evaluated.

Note also that no held-out validation set was used, so the reported loss measures fit to the training corpus rather than generalization.

Corpus

38.784 B tokens across 19 sources, Qwen3-tokenized and packed into 4,096-token sequences. The run consumed 41.00 B tokens, i.e. 1.057 epochs — the final ~3% of training revisited already-seen data.

Domain	Tokens	Share
Korean	16.462 B	42.45%
English / other	11.524 B	29.71%
Code	5.890 B	15.19%
Science / math	4.908 B	12.66%

The Korean share comes from three web-derived sources: fineweb2_korean (10.791 B), culturax_ko (5.300 B) and korean_webtext (0.371 B). There is no Korean books, encyclopedic or academic source in the corpus — relevant when interpreting Korean knowledge results, since 97.7% of Korean tokens come from two large web crawls.

Excluded on licensing grounds before training: NamuWiki (CC BY-NC-SA), Falcon-RefinedWeb, and arXiv (mixed per-paper licensing).

Evaluation

This checkpoint: not evaluated.

The numbers below are from the 3 B-token pilot — a separate, earlier run sharing the same recipe and manifest at 1/13.7 the token count — against Qwen3-8B-Base on lm-evaluation-harness with identical settings. They are included to show the recipe was validated before the main run, not as results for this model.

Track	Mean Δ vs Qwen3-8B-Base
Korean (KMMLU, HaeRae, KoBEST ×4)	+0.545 pt
English (MMLU, ARC-C, HellaSwag, WinoGrande)	+0.620 pt

Gate criterion — Korean up AND English within −0.5 pt — passed.

How to read that honestly. KMMLU (+0.03, n=35,030) and HaeRae (+0.09) — the two substantive Korean knowledge benchmarks — were flat. KoBEST BoolQ contributes +5.91 while the other five Korean benchmarks sum to −2.64; without BoolQ the Korean mean is negative (−0.528). BoolQ is a two-class task, where a shift of that size is as consistent with calibration change as with knowledge gain.

What the pilot established is the negative result — that the recipe does not damage the model (English held and improved). It did not establish that Korean capability improves by a useful margin.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

m = "mkd-hossain/Keural-Cortex-8B-Base"
tok = AutoTokenizer.from_pretrained(m)
model = AutoModelForCausalLM.from_pretrained(m, torch_dtype="bfloat16", device_map="cuda")

# Base model: complete text, do not instruct.
out = model.generate(**tok("대한민국의 수도는", return_tensors="pt").to("cuda"), max_new_tokens=64)
print(tok.decode(out[0], skip_special_tokens=True))

CPU inference

Quantized GGUF via llama.cpp is dramatically faster than eager transformers on CPU. Measured on 2 × Xeon Platinum 8570 (AMX), 16 threads, on this project's step-5000 checkpoint:

Format	Size	Generation
Q8_0	8.1 GiB	17.6 tok/s
Q4_K_M	4.7 GiB	15.7 tok/s
F16	15.3 GiB	8.2 tok/s
PyTorch eager bf16	15.3 GiB	0.43 tok/s

Build llama.cpp with -DGGML_NATIVE=ON or the AMX/AVX512 kernels are not compiled in. On AMX-INT8 hardware Q8_0 outperforms Q4_K_M despite being larger.

Related artifacts

Model	Tokens	Purpose
this	41.00 B	final CPT checkpoint
`Keural-Cortex-8B-step10000`	31.46 B	intermediate (77%)
`Keural-Cortex-8B-step5000`	15.73 B	intermediate (38%)
`Keural-Cortex-8B-pilot`	3.00 B	recipe validation run

Full audit record — configuration, training dynamics, corpus provenance, evaluation, findings, and reproduction instructions — is published at github.com/MKD-CORP/Keural-Cortex-8B under CPT_details/.

Roadmap

Phase 5 (context extension 32K → 64K via YaRN) and Phase 6 (post-training: SFT then DPO) are planned but not yet performed. Tool calling and instruction following require Phase 6.

License

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

Intended use

Research, and as a base for further fine-tuning. Not suitable as a user-facing assistant — it has no instruction tuning — and not a basis for capability claims until evaluated and decontaminated.

Downloads last month: 156

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for mkd-hossain/Keural-Cortex-8B-Base

Base model

Qwen/Qwen3-8B-Base

Finetuned

(561)

this model

Quantizations

1 model