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

Libraries

How to use PoSTMEDIA/Lux-V2 with Transformers:

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

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PoSTMEDIA/Lux-V2"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Lux-V2

Lux-V2 is a Korean-enhanced, fully fine-tuned LLM built on top of google/gemma-4-26B-A4B-it by PoSTMEDIA AI Lab, succeeding Lux-V1.

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. Compared to Lux-V1, the V2 generation delivers a large, across-the-board benchmark improvement while adding substantial Korean domain knowledge.

Highlights

Large upgrade over Lux-V1 — wins on 16 of 18 internal benchmarks, with competition-math gains of +10 to +20 points
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 — unlike naive full fine-tuning, the V2 methodology keeps (and on most axes improves) the base model's abilities
MoE efficiency — 26B total / ~4B active parameters at serving time
Verified training data — the synthetic datasets are produced with execution- and rule-based verification pipelines rather than unfiltered generation

Model Overview

Specification	Details
Base Model	`google/gemma-4-26B-A4B-it`
Parameters	26B total / ~4B active
Architecture	Decoder-only Transformer (MoE)
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

Lux-V1 demonstrated that a full fine-tune could adapt Gemma-4 without destroying it. Lux-V2 goes further: it adapts the model to Korean domains while measurably improving general capability. All results below were measured in-house under a single unified protocol (identical prompts, sampling, and generation budgets for both models).

Benchmark	gemma-4-26B-A4B-it (base)	Lux-V1	Lux-V2
MMLU	84.2	82.0	83.8
AIME 2024	90.0	73.3	90.0
AIME 2025	73.3	60.0	80.0
HMMT 2025	66.7	36.7	56.7
IFEval	89.6	88.5	91.7
KMMLU	72.8	70.7	72.9
KMMLU-Pro	72.0	67.8	70.6
CLIcK	81.3	79.8	82.0
KoBALT	68.3	59.1	65.9
HAE-RAE Bench	79.7	76.8	80.1
HRM8K	83.5	79.9	83.6
KoSimpleQA	86.7	83.7	87.9
KoSQA-EM	33.6	28.7	34.1
Average (all 18 benchmarks)	74.5	69.1	74.8

The full 18-benchmark suite shows 16 wins and 2 minor regressions (GSM8K −1.9, GPQA −1.0) versus Lux-V1. Averaged over all 18 benchmarks, Lux-V2 scores 74.8 — above both the original Gemma-4 base (74.5) and Lux-V1 (69.1) — i.e., the Korean domain adaptation comes with a net gain over the unmodified base model, not a trade-off.

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"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{"role": "user", "content": "경복궁의 역사적 의미를 세 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

Korean-first assistants that must retain strong general reasoning
Korean domain QA (law, cultural heritage, tourism) with source-grounded knowledge
Mathematical and coding assistance in Korean and English
Drop-in upgrade for existing Lux-V1 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{lux2026,
  title  = {Lux-V2: Capability-Preserving Korean Domain Adaptation of Gemma-4},
  author = {{PoSTMEDIA AI Lab}},
  year   = {2026},
  url    = {https://huggingface.co/PoSTMEDIA/Lux-V2}
}

Contact

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

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Safetensors

Model size

829k params

Tensor type

BF16

Model tree for PoSTMEDIA/Lux-V2

Base model

google/gemma-4-26B-A4B

Finetuned

google/gemma-4-26B-A4B-it

Finetuned

(165)

this model

Quantizations

1 model