Instructions to use Hikaf143/Keural-Nova-1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hikaf143/Keural-Nova-1.1 with Transformers:

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

pipe = pipeline("text-generation", model="Hikaf143/Keural-Nova-1.1")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Hikaf143/Keural-Nova-1.1")
model = AutoModelForMultimodalLM.from_pretrained("Hikaf143/Keural-Nova-1.1", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Hikaf143/Keural-Nova-1.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hikaf143/Keural-Nova-1.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hikaf143/Keural-Nova-1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Hikaf143/Keural-Nova-1.1

SGLang

How to use Hikaf143/Keural-Nova-1.1 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 "Hikaf143/Keural-Nova-1.1" \
    --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": "Hikaf143/Keural-Nova-1.1",
		"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 "Hikaf143/Keural-Nova-1.1" \
        --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": "Hikaf143/Keural-Nova-1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Hikaf143/Keural-Nova-1.1 with Docker Model Runner:
```
docker model run hf.co/Hikaf143/Keural-Nova-1.1
```

Keural-Nova-1.1 / README.md

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---
license: apache-2.0
base_model: Qwen/Qwen3.6-35B-A3B
language:
  - ko
  - en
library_name: transformers
pipeline_tag: text-generation
tags:
  - korean
  - moe
  - lora
  - instruction-tuning
  - keural
---
# Keural Nova 1.1

**Keural Nova** is a Korean-focused instruction-tuned model built by MKD on top of
[Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) (Apache 2.0), a
35B-parameter Mixture-of-Experts model with ~3B active parameters per token. Full credit for
the base architecture and pretraining goes to the Qwen team at Alibaba Cloud — this release
is a supervised fine-tune (LoRA, merged into the base weights) targeting Korean
conversational quality, with an English/general replay mix to limit capability regression.

**Version note:** v1.1 is the full-training endpoint (2 epochs, 15,846 optimizer steps). The
sibling release **Keural Nova v1.0** is the end-of-epoch-1 checkpoint (7,500 steps, best
held-out eval loss). The two make slightly different trade-offs — v1.1 scores marginally
higher on HAE-RAE and MMLU, v1.0 on KoBEST and HumanEval — and we publish both transparently
so users can pick empirically.

![benchmark comparison](assets/benchmarks.png)

![animated comparison](assets/benchmarks.gif)

## Evaluation

All numbers measured by us with lm-evaluation-harness 0.4.12 (vLLM backend, bf16, seed 42,
default few-shot settings), same harness and settings for base and fine-tuned models.

| Benchmark | Suite | Base model | v1.0 | **v1.1 (this model)** | Δ v1.1 vs base |
|---|---|---:|---:|---:|---|
| KoBEST | Korean | 62.88 | 68.82 | **67.29** | **+4.41** |
| HAE-RAE | Korean | 75.53 | 73.42 | 74.52 | −1.01 |
| KMMLU | Korean | 64.46 | 62.52 | 62.38 | −2.08 |
| MMLU | General | 83.83 | 82.07 | 82.37 | −1.46 |
| GSM8K (strict-match) | Math | 32.15* | 87.11 | **86.88** | +54.73* |
| HumanEval (pass@1) | Code | 62.20 | 20.73 | 11.59 | **−50.61** |

\* The base model's GSM8K score is heavily depressed by answer-format mismatch under
strict-match extraction; the post-SFT jump is largely format compliance. Treat the delta as
a formatting improvement, not a ~55-point reasoning gain.

**Honest summary:** the fine-tune substantially improves Korean conversational/comprehension
performance (KoBEST) and answer formatting, at the cost of small regressions on
knowledge-recall benchmarks (KMMLU, HAE-RAE, MMLU) and a large regression on code
generation — see Limitations. If your workload is Korean dialogue/comprehension, this model
is the point of this release; if it is knowledge QA or coding, use the base model.

## Intended use & limitations

- **Intended:** Korean conversational AI, Korean text comprehension and generation,
  bilingual (ko/en) assistant use, RAG-style grounded answering in Korean.
- **NOT suitable for code generation.** HumanEval drops from 62.2 → 11.6 pass@1 (v1.0
  retains 20.7). Root cause (identified, not speculative): a large fraction of code examples
  in one upstream training source had whitespace/indentation corrupted during that dataset's
  original export, and the model learned the corrupted formatting. A future release will
  address this with repaired data.
- Knowledge-heavy benchmarks (KMMLU/HAE-RAE/MMLU) regress by ~1–2 points vs base.
- **Context length:** SFT sequences were up to 8,192 tokens; the base architecture supports
  262,144 tokens natively and **up to 1,000,000 tokens via YaRN** rope scaling. Extended-context
  **serving up to 1M tokens has been run in production** through MKD's OpenAI-compatible gateway
  (YaRN factor 4.0) and works. Note: long-context *answer accuracy* has not been measured with a
  dedicated benchmark (e.g., needle-in-a-haystack / RULER), so validate for your specific
  long-document workload.
- The model inherits the base model's multimodal architecture (vision tower is present and
  untouched), but this fine-tune neither trained nor evaluated vision — treat it as a text
  model.

## Training details

| | |
|---|---|
| Base model | Qwen/Qwen3.6-35B-A3B @ `995ad96e` |
| Method | LoRA (rank 32, α 64, dropout 0.0), merged into base weights |
| Adapted modules | attention (`q/k/v/o_proj`), Gated-DeltaNet linear attention (`in_proj_qkv/z/b/a`, `out_proj`), shared experts, **and all 256 routed experts per layer** (fused `gate_up_proj`/`down_proj` tensors via PEFT `target_parameters`) |
| Deliberately frozen | MoE router (`mlp.gate`), `shared_expert_gate`, embeddings, LM head, vision tower |
| Data | 253,528 examples (~85% Korean / 15% English replay), 13 sources, ChatML, empty-`<think>` (non-thinking) format |
| Schedule | effective batch 32, LR 1e-4 cosine, warmup 5%, bf16, DeepSpeed ZeRO-2, 2× H200 |
| This checkpoint | step 15,846 (2 full epochs) — final training state, train loss 0.688 |
| Loss masking | assistant tokens only; empty think-block scaffold excluded from loss |

Router-integrity and expert-targeting were verified programmatically before and after
training (the router and gates are bit-identical to the base model in this release; the
routed-expert weights are where most of the adaptation lives).

## Training data & licensing

Sources (per-row license tags preserved from upstream): Korean conversation/RAG/QA and
instruction data from the `mkd-chanwoo` keural datasets (CC BY 4.0 / MIT / Apache-2.0 /
CC BY-SA 4.0 subsets only), plus eight sources from `Mkd-Yonas/keural-SFT-chatml-ko-v1`
(MIT / Apache-2.0 / CC BY 4.0), plus English replay data (ultrachat, openorca, alpaca,
magicoder, mathinstruct, gsm8k — MIT/CC BY 4.0).

License mix of the final training set: CC BY 4.0 (105k rows), MIT (87k), Apache-2.0 (46k),
CC BY-SA 4.0 (3.5k), unlabeled (13.7k — two Korean instruction subsets whose upstream rows
carry `license: unknown`; upstream documentation states Apache-2.0, verification pending).

**Excluded by policy:** all AI Hub-derived subsets (non-commercial research terms) were
filtered out of training data.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "mkd-ai/Keural-Nova-v1.1"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")
messages = [{"role": "user", "content": "한국의 전통 발효 음식에 대해 설명해 주세요."}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True,
                                 enable_thinking=False, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=512, temperature=0.7, top_p=0.95)
print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
```

vLLM serving (this is a fully merged model — no adapter loading needed):

```bash
vllm serve mkd-ai/Keural-Nova-1.1 --tensor-parallel-size 2 --dtype bfloat16
```

The model is trained in the base model's **non-thinking** chat format
(`enable_thinking=False`); thinking-mode behavior is inherited from the base model untouched.

## Acknowledgements

Built on **Qwen3.6-35B-A3B** by the Qwen team (Alibaba Cloud), released under Apache 2.0.
Korean datasets curated by the MKD data team. Fine-tuning, evaluation, and release
engineering by MKD.

	---
	license: apache-2.0
	base_model: Qwen/Qwen3.6-35B-A3B
	language:
	- ko
	- en
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- korean
	- moe
	- lora
	- instruction-tuning
	- keural
	---

	# Keural Nova 1.1

	Keural Nova is a Korean-focused instruction-tuned model built by MKD on top of
	[Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) (Apache 2.0), a
	35B-parameter Mixture-of-Experts model with ~3B active parameters per token. Full credit for
	the base architecture and pretraining goes to the Qwen team at Alibaba Cloud — this release
	is a supervised fine-tune (LoRA, merged into the base weights) targeting Korean
	conversational quality, with an English/general replay mix to limit capability regression.

	Version note: v1.1 is the full-training endpoint (2 epochs, 15,846 optimizer steps). The
	sibling release Keural Nova v1.0 is the end-of-epoch-1 checkpoint (7,500 steps, best
	held-out eval loss). The two make slightly different trade-offs — v1.1 scores marginally
	higher on HAE-RAE and MMLU, v1.0 on KoBEST and HumanEval — and we publish both transparently
	so users can pick empirically.

	![benchmark comparison](assets/benchmarks.png)

	![animated comparison](assets/benchmarks.gif)

	## Evaluation

	All numbers measured by us with lm-evaluation-harness 0.4.12 (vLLM backend, bf16, seed 42,
	default few-shot settings), same harness and settings for base and fine-tuned models.

	\| Benchmark \| Suite \| Base model \| v1.0 \| v1.1 (this model) \| Δ v1.1 vs base \|
	\|---\|---\|---:\|---:\|---:\|---\|
	\| KoBEST \| Korean \| 62.88 \| 68.82 \| 67.29 \| +4.41 \|
	\| HAE-RAE \| Korean \| 75.53 \| 73.42 \| 74.52 \| −1.01 \|
	\| KMMLU \| Korean \| 64.46 \| 62.52 \| 62.38 \| −2.08 \|
	\| MMLU \| General \| 83.83 \| 82.07 \| 82.37 \| −1.46 \|
	\| GSM8K (strict-match) \| Math \| 32.15* \| 87.11 \| 86.88 \| +54.73* \|
	\| HumanEval (pass@1) \| Code \| 62.20 \| 20.73 \| 11.59 \| −50.61 \|

	\* The base model's GSM8K score is heavily depressed by answer-format mismatch under
	strict-match extraction; the post-SFT jump is largely format compliance. Treat the delta as
	a formatting improvement, not a ~55-point reasoning gain.

	Honest summary: the fine-tune substantially improves Korean conversational/comprehension
	performance (KoBEST) and answer formatting, at the cost of small regressions on
	knowledge-recall benchmarks (KMMLU, HAE-RAE, MMLU) and a large regression on code
	generation — see Limitations. If your workload is Korean dialogue/comprehension, this model
	is the point of this release; if it is knowledge QA or coding, use the base model.

	## Intended use & limitations

	- Intended: Korean conversational AI, Korean text comprehension and generation,
	bilingual (ko/en) assistant use, RAG-style grounded answering in Korean.
	- NOT suitable for code generation. HumanEval drops from 62.2 → 11.6 pass@1 (v1.0
	retains 20.7). Root cause (identified, not speculative): a large fraction of code examples
	in one upstream training source had whitespace/indentation corrupted during that dataset's
	original export, and the model learned the corrupted formatting. A future release will
	address this with repaired data.
	- Knowledge-heavy benchmarks (KMMLU/HAE-RAE/MMLU) regress by ~1–2 points vs base.
	- Context length: SFT sequences were up to 8,192 tokens; the base architecture supports
	262,144 tokens natively and up to 1,000,000 tokens via YaRN rope scaling. Extended-context
	serving up to 1M tokens has been run in production through MKD's OpenAI-compatible gateway
	(YaRN factor 4.0) and works. Note: long-context answer accuracy has not been measured with a
	dedicated benchmark (e.g., needle-in-a-haystack / RULER), so validate for your specific
	long-document workload.
	- The model inherits the base model's multimodal architecture (vision tower is present and
	untouched), but this fine-tune neither trained nor evaluated vision — treat it as a text
	model.

	## Training details

	\| \| \|
	\|---\|---\|
	\| Base model \| Qwen/Qwen3.6-35B-A3B @ `995ad96e` \|
	\| Method \| LoRA (rank 32, α 64, dropout 0.0), merged into base weights \|
	\| Adapted modules \| attention (`q/k/v/o_proj`), Gated-DeltaNet linear attention (`in_proj_qkv/z/b/a`, `out_proj`), shared experts, and all 256 routed experts per layer (fused `gate_up_proj`/`down_proj` tensors via PEFT `target_parameters`) \|
	\| Deliberately frozen \| MoE router (`mlp.gate`), `shared_expert_gate`, embeddings, LM head, vision tower \|
	\| Data \| 253,528 examples (~85% Korean / 15% English replay), 13 sources, ChatML, empty-`<think>` (non-thinking) format \|
	\| Schedule \| effective batch 32, LR 1e-4 cosine, warmup 5%, bf16, DeepSpeed ZeRO-2, 2× H200 \|
	\| This checkpoint \| step 15,846 (2 full epochs) — final training state, train loss 0.688 \|
	\| Loss masking \| assistant tokens only; empty think-block scaffold excluded from loss \|

	Router-integrity and expert-targeting were verified programmatically before and after
	training (the router and gates are bit-identical to the base model in this release; the
	routed-expert weights are where most of the adaptation lives).

	## Training data & licensing

	Sources (per-row license tags preserved from upstream): Korean conversation/RAG/QA and
	instruction data from the `mkd-chanwoo` keural datasets (CC BY 4.0 / MIT / Apache-2.0 /
	CC BY-SA 4.0 subsets only), plus eight sources from `Mkd-Yonas/keural-SFT-chatml-ko-v1`
	(MIT / Apache-2.0 / CC BY 4.0), plus English replay data (ultrachat, openorca, alpaca,
	magicoder, mathinstruct, gsm8k — MIT/CC BY 4.0).

	License mix of the final training set: CC BY 4.0 (105k rows), MIT (87k), Apache-2.0 (46k),
	CC BY-SA 4.0 (3.5k), unlabeled (13.7k — two Korean instruction subsets whose upstream rows
	carry `license: unknown`; upstream documentation states Apache-2.0, verification pending).

	Excluded by policy: all AI Hub-derived subsets (non-commercial research terms) were
	filtered out of training data.

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "mkd-ai/Keural-Nova-v1.1"
	tok = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")

	messages = [{"role": "user", "content": "한국의 전통 발효 음식에 대해 설명해 주세요."}]
	inputs = tok.apply_chat_template(messages, add_generation_prompt=True,
	enable_thinking=False, return_tensors="pt").to(model.device)
	out = model.generate(inputs, max_new_tokens=512, temperature=0.7, top_p=0.95)
	print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
	```

	vLLM serving (this is a fully merged model — no adapter loading needed):

	```bash
	vllm serve mkd-ai/Keural-Nova-1.1 --tensor-parallel-size 2 --dtype bfloat16
	```

	The model is trained in the base model's non-thinking chat format
	(`enable_thinking=False`); thinking-mode behavior is inherited from the base model untouched.

	## Acknowledgements

	Built on Qwen3.6-35B-A3B by the Qwen team (Alibaba Cloud), released under Apache 2.0.
	Korean datasets curated by the MKD data team. Fine-tuning, evaluation, and release
	engineering by MKD.