Instructions to use PoSTMEDIA/Lux-V2-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PoSTMEDIA/Lux-V2-Pro with Transformers:

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

pipe = pipeline("text-generation", model="PoSTMEDIA/Lux-V2-Pro")
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("PoSTMEDIA/Lux-V2-Pro")
model = AutoModelForMultimodalLM.from_pretrained("PoSTMEDIA/Lux-V2-Pro", 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 PoSTMEDIA/Lux-V2-Pro with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PoSTMEDIA/Lux-V2-Pro"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PoSTMEDIA/Lux-V2-Pro",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PoSTMEDIA/Lux-V2-Pro

SGLang

How to use PoSTMEDIA/Lux-V2-Pro 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 "PoSTMEDIA/Lux-V2-Pro" \
    --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": "PoSTMEDIA/Lux-V2-Pro",
		"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 "PoSTMEDIA/Lux-V2-Pro" \
        --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": "PoSTMEDIA/Lux-V2-Pro",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PoSTMEDIA/Lux-V2-Pro with Docker Model Runner:
```
docker model run hf.co/PoSTMEDIA/Lux-V2-Pro
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Lux-V2-Pro

Lux-V2-Pro is a Korean-enhanced, fully fine-tuned LLM built on top of google/gemma-4-31B-it by PoSTMEDIA AI Lab, succeeding Lux-V1-Pro.

It is produced with the second generation of PoSTMEDIA's in-house Capability-Preserving Full Fine-Tuning research — a training methodology designed so that deep domain adaptation does not erode the reasoning, instruction-following, and multilingual abilities of the base model. As the maximum-capability tier of the Lux line, V2-Pro improves on Lux-V1-Pro across the board while adding substantial Korean domain knowledge.

Highlights

Upgrade over Lux-V1-Pro on 16 of 18 internal benchmarks — including AIME 2024 93.3 (+13.3) and AIME 2025 +13.4
Korean domain knowledge built in — trained on PoSTMEDIA's in-house Korean synthetic datasets spanning seven domains: general conversation, coding, instruction following, law, cultural heritage, tourism, and mathematics, plus the PoSTMEDIA identity dataset
Base capability preserved and improved — general knowledge (MMLU 86.6) and instruction following (IFEval 93.7) end above the V1-Pro level
Dense 31B — the strongest reasoning tier of the Lux family
Verified training data — synthetic datasets produced with execution- and rule-based verification pipelines

Model Overview

Specification	Details
Base Model	`google/gemma-4-31B-it`
Parameters	31B (dense)
Architecture	Decoder-only Transformer (dense)
Training Precision	BF16
Inference Precision	BF16
Context Length	Inherits from Gemma-4 base
Fine-Tuning Method	Full-parameter SFT (Capability-Preserving recipe, 2nd gen)
Languages	Korean, English

What's New vs Lux-V1-Pro

All results below were measured in-house under a single unified protocol (identical prompts, sampling, and generation budgets for both models).

Benchmark	gemma-4-31B-it (base)	Lux-V1-Pro	Lux-V2-Pro
MMLU	86.6	85.9	86.6
AIME 2024	83.3	80.0	93.3
AIME 2025	66.7	63.3	76.7
HMMT 2025	70.0	60.0	63.3
IFEval	93.2	92.4	93.7
KMMLU	76.6	74.8	76.9
KMMLU-Pro	78.2	77.0	77.4
CLIcK	85.1	84.8	85.5
KoBALT	73.4	71.9	73.9
HAE-RAE Bench	82.0	80.6	83.1
HRM8K	85.8	84.7	85.8
KoSimpleQA	91.2	90.0	90.8
KoSQA-EM	38.0	36.1	38.5
Average (17 common benchmarks)	76.6	75.5	78.2

The full 18-benchmark suite shows 16 wins, 1 tie (GPQA), and 2 minor regressions (GSM8K −1.2, IFBench −0.2) versus Lux-V1-Pro. Averaged over the 17 benchmarks measured for all three models, Lux-V2-Pro scores 78.2 — above both the original Gemma-4 base (76.6) and Lux-V1-Pro (75.5). (MMLU-Pro was not measured for the base model due to generation-budget constraints.)

Training Data

The V2 generation is trained on PoSTMEDIA's in-house Korean synthetic data assets, generated and quality-controlled by our internal data factory:

General conversation — natural Korean multi-topic dialogue
Coding — execution-verified code generation and explanation
Instruction following — rule-verifiable Korean constraint-following tasks
Law — source-grounded Korean legal knowledge QA
Cultural heritage — source-grounded Korean heritage knowledge QA
Tourism — source-grounded Korean tourism knowledge QA
Mathematics — symbolically verified Korean math reasoning
PoSTMEDIA identity — hand-curated identity dataset

Correctness of the synthetic data is enforced by verification gates (code execution, symbolic math equivalence, rule checkers, and source-grounding checks) rather than by model self-judgment.

The exact training procedure — schedule, module selection, and the post-training consolidation step that preserves base capability — is an internal research method and is not disclosed in detail.

Quick Start

pip install transformers accelerate

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "PoSTMEDIA/Lux-V2-Pro"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{"role": "user", "content": "문화재보호법의 목적을 두 문장으로 설명해줘."}]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

Use Cases

The most demanding Korean reasoning and generation workloads in the Lux line
Korean domain QA (law, cultural heritage, tourism) with source-grounded knowledge
Competition-level mathematical assistance
Drop-in upgrade for existing Lux-V1-Pro deployments

Safety & Limitations

The model can generate incorrect or outdated information; verify high-stakes outputs.
Korean domain knowledge reflects the training data snapshot and may not cover recent changes (e.g., amended laws).
Inherits the general limitations and usage considerations of the Gemma-4 base model.

Citation

@misc{lux2026pro,
  title  = {Lux-V2-Pro: Capability-Preserving Korean Domain Adaptation of Gemma-4},
  author = {{PoSTMEDIA AI Lab}},
  year   = {2026},
  url    = {https://huggingface.co/PoSTMEDIA/Lux-V2-Pro}
}

Contact

Questions and feedback — please open a discussion on the model page.

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Safetensors

Model size

1.46M params

Tensor type

BF16

Model tree for PoSTMEDIA/Lux-V2-Pro

Base model

google/gemma-4-31B

Finetuned

google/gemma-4-31B-it

Finetuned

(250)

this model

Quantizations

1 model