Instructions to use mkd-ai/Keural-Nova-v1.2-experimental-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mkd-ai/Keural-Nova-v1.2-experimental-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="mkd-ai/Keural-Nova-v1.2-experimental-NVFP4")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("mkd-ai/Keural-Nova-v1.2-experimental-NVFP4")
model = AutoModelForMultimodalLM.from_pretrained("mkd-ai/Keural-Nova-v1.2-experimental-NVFP4", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mkd-ai/Keural-Nova-v1.2-experimental-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mkd-ai/Keural-Nova-v1.2-experimental-NVFP4"
# 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-ai/Keural-Nova-v1.2-experimental-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mkd-ai/Keural-Nova-v1.2-experimental-NVFP4

SGLang

How to use mkd-ai/Keural-Nova-v1.2-experimental-NVFP4 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-ai/Keural-Nova-v1.2-experimental-NVFP4" \
    --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-ai/Keural-Nova-v1.2-experimental-NVFP4",
		"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-ai/Keural-Nova-v1.2-experimental-NVFP4" \
        --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-ai/Keural-Nova-v1.2-experimental-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mkd-ai/Keural-Nova-v1.2-experimental-NVFP4 with Docker Model Runner:
```
docker model run hf.co/mkd-ai/Keural-Nova-v1.2-experimental-NVFP4
```

Keural Nova v1.2 — NVFP4 (experimental)

NVFP4 (4-bit) quantization of Keural Nova v1.2, by MKD, for high-throughput serving on NVIDIA Blackwell GPUs via vLLM.

67 GB (bf16) → 21.9 GB (NVFP4) — ~3× smaller.
Recipe replicates RedHatAI/Qwen3.6-35B-A3B-NVFP4: NVFP4 4-bit weights + activations on the language model; vision tower, lm_head, and MTP kept in high precision for accuracy.
Format: compressed-tensors / nvfp4-pack-quantized → served directly by vLLM.

⚠️ Hardware

FP4 tensor cores exist only on Blackwell (B200 / GB200 / RTX 50xx). Serve there for the speed/memory benefit. (Hopper/H200 lack FP4 kernels — this checkpoint is produced on H200 but runs accelerated on Blackwell.)

Serving (vLLM on Blackwell)

vllm serve mkd-hossain/Keural-Nova-v1.2-experimental-NVFP4 \
  --served-model-name Keural-Nova-v1.2 \
  --tensor-parallel-size 2 \
  --max-model-len 262144 \
  --tool-call-parser qwen3_xml --enable-auto-tool-choice

Non-thinking default (the fixed chat template ships here); tool calls are Qwen XML.
Serve native 256K for agent/tool workloads (YaRN 1M only for genuine >256K requests).
Multimodal (vision) works — the vision tower is kept high-precision.

Quantization details

Tool: llm-compressor (compressed-tensors), NVFP4 scheme.
Calibration: 512 samples from the Keural Nova v1.2 SFT mix (Korean/English/code/tool-use), seq-len 2048.
Coverage: 31,070 linear layers quantized (incl. 30,880 MoE experts + attention + Gated-DeltaNet linear-attention); vision blocks + lm_head + MTP excluded (bf16).
Group scheme: weights 4-bit float / activations 4-bit float, per-block FP8 scales (NVFP4).

Accuracy

NVFP4 typically costs ~1–2% vs bf16 — the same small delta as the public nvidia/RedHatAI NVFP4 Qwen3.6-A3B checkpoints. Validate on your Blackwell against the bf16 base (Keural-Nova-v1.2-experimental) on your workload (tool-calling, Korean, code) before production.

License & attribution

Apache-2.0, inheriting the base Qwen/Qwen3.6-35B-A3B license. Quantization recipe after RedHatAI/Qwen3.6-35B-A3B-NVFP4. "Keural Nova" is a model by MKD.

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Safetensors

Model size

35B params

Tensor type

BF16

Model tree for mkd-ai/Keural-Nova-v1.2-experimental-NVFP4

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

mkd-hossain/Keural-Nova-v1.2-experimental

Quantized

(4)

this model