Instructions to use PoSTMEDIA/Xin-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PoSTMEDIA/Xin-V1 with Transformers:

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

pipe = pipeline("text-generation", model="PoSTMEDIA/Xin-V1")
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/Xin-V1")
model = AutoModelForMultimodalLM.from_pretrained("PoSTMEDIA/Xin-V1", 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/Xin-V1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PoSTMEDIA/Xin-V1"
# 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/Xin-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PoSTMEDIA/Xin-V1

SGLang

How to use PoSTMEDIA/Xin-V1 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/Xin-V1" \
    --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/Xin-V1",
		"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/Xin-V1" \
        --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/Xin-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PoSTMEDIA/Xin-V1 with Docker Model Runner:
```
docker model run hf.co/PoSTMEDIA/Xin-V1
```

Xin-V1

Xin-V1 is a Korean-enhanced, instruction-tuned LLM built on top of Qwen/Qwen3.8-27B by PoSTMEDIA AI Lab — the first model of the Xin line.

It is produced with PoSTMEDIA's in-house Capability-Preserving Full Fine-Tuning research (the same second-generation methodology behind the Lux-V2 family) — a training methodology designed so that deep domain adaptation does not erode the reasoning, instruction-following, and multilingual abilities of the base model. Xin-V1 is tuned as a non-thinking (direct answer) product: it responds immediately without emitting reasoning traces.

Highlights

Competition math significantly improved — AIME 2024/2025/2026 and HMMT 2025 average up strongly over the base model, measured across 3 sampling seeds
Base capability preserved — general knowledge (MMLU/MMLU-Pro), GPQA, and instruction following stay at or near base level
Korean-first tuning — trained on PoSTMEDIA's in-house verified Korean synthetic datasets
Non-thinking product — fast direct answers; no <think> traces
Hybrid-attention 27B — 16 full-attention + 48 linear-attention layers, efficient long-context serving

Model Overview

Specification	Details
Base Model	`Qwen/Qwen3.8-27B`
Parameters	27B (hybrid attention: 16 full + 48 linear layers)
Training Precision	BF16
Inference Precision	BF16
Context Length	Inherits from Qwen3.8 base
Fine-Tuning Method	Full-parameter SFT (Capability-Preserving recipe)
Mode	Non-thinking (direct answer)
Languages	Korean, English

Benchmarks vs Base

All results measured in-house under a single unified protocol (identical prompts, official non-thinking sampling — temperature 0.7, top-p 0.8, top-k 20, presence penalty 1.5 — and identical generation budgets). Competition-math rows (AIME/HMMT) are the mean of 3 sampling seeds for both models to suppress small-sample noise.

Benchmark	Qwen3.8-27B (base)	Xin-V1
MMLU	84.5	83.5
MMLU-Pro	82.1	82.0
GPQA	80.3	81.3
GSM8K	86.1	86.8
AIME 2024†	77.8	83.3
AIME 2025†	71.1	71.1
AIME 2026†	78.9	81.1
HMMT 2025†	56.7	54.4
IFEval	86.7	83.7
IFBench	71.9	74.5
KMMLU	72.1	70.9
KMMLU-Pro	65.8	66.5
CLIcK	77.5	76.0
KoBALT	51.1	47.1
HAE-RAE Bench	79.9	78.4
HRM8K	83.5	83.3
KoSimpleQA‡	56.4	66.5
KoSQA-EM	16.3	15.7
Average (all 18)	71.0	71.5

† Competition-math rows are the mean of 3 sampling seeds for both models. ‡ Judge-scored short-answer QA; Xin-V1's more direct answer style contributes to this gain (exact-match on the same set is comparable to base).

Training Data

Xin-V1 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
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) 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/Xin-V1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [{"role": "user", "content": "한국의 전통 명절 세 가지를 소개해줘."}]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, enable_thinking=False,
    return_tensors="pt",
).to(model.device)
outputs = model.generate(
    inputs, max_new_tokens=512,
    do_sample=True, temperature=0.7, top_p=0.8, top_k=20,
)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

Note: Xin-V1 is tuned for non-thinking use. Pass enable_thinking=False to apply_chat_template (as above) and use the official non-thinking sampling parameters (temperature 0.7, top-p 0.8, top-k 20, presence penalty 1.5).

Use Cases

Korean-first assistants requiring fast, direct answers
Mathematical and coding assistance in Korean and English
General Korean instruction-following workloads

Safety & Limitations

The model can generate incorrect or outdated information; verify high-stakes outputs.
Inherits the general limitations and usage considerations of the Qwen3.8 base model.

Citation

@misc{xin2026,
  title  = {Xin-V1: Capability-Preserving Korean Fine-Tuning of Qwen3.8},
  author = {{PoSTMEDIA AI Lab}},
  year   = {2026},
  url    = {https://huggingface.co/PoSTMEDIA/Xin-V1}
}

Contact

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

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