Instructions to use PoSTMEDIA/Rosetta-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PoSTMEDIA/Rosetta-7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="PoSTMEDIA/Rosetta-7B-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("PoSTMEDIA/Rosetta-7B-Instruct", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PoSTMEDIA/Rosetta-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PoSTMEDIA/Rosetta-7B-Instruct"
# 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/Rosetta-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PoSTMEDIA/Rosetta-7B-Instruct

SGLang

How to use PoSTMEDIA/Rosetta-7B-Instruct 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/Rosetta-7B-Instruct" \
    --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/Rosetta-7B-Instruct",
		"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/Rosetta-7B-Instruct" \
        --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/Rosetta-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PoSTMEDIA/Rosetta-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/PoSTMEDIA/Rosetta-7B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Rosetta-7B-Instruct

Introduction

Rosetta-7B-Instruct is a 7B-parameter bilingual (Korean-English) instruction-tuned model developed by PoSTMEDIA. Built on PoSTMEDIA's Rosetta dense decoder-only architecture, it is post-trained from Rosetta-7B-Base through large-scale supervised fine-tuning and preference optimization, with a deliberate focus on the capabilities that matter most in real Korean-language deployments: instruction following, Korean cultural and factual knowledge, and Korean mathematical reasoning.

Rosetta-7B is a three-model family covering the full spectrum from research to production:

Model	Download	Note
Rosetta-7B-Base	HuggingFace	Foundation model (completion-style)
Rosetta-7B-Instruct	HuggingFace	Instruction following / chat (this model)
Rosetta-7B-Instruct-NVFP4	HuggingFace	NVFP4 4-bit of Instruct (NVIDIA Blackwell / DGX Spark)
Rosetta-7B-Think	HuggingFace	Explicit reasoning (`<think>`)
Rosetta-7B-Think-NVFP4	HuggingFace	NVFP4 4-bit of Think (NVIDIA Blackwell / DGX Spark)

Highlights

Korean math reasoning leader — HRM8K 60.4, the top score in our eight-model same-protocol comparison of Korean and global open models
Top-tier instruction following — IFEval 80.4, within the leading group of Korean open models
Korean-first design — 161K Korean-extended vocabulary, dedicated Korean continual-pretraining stage, and in-house synthetic Korean data assets
Strong Korean factuality — KoSimpleQA 71.7
65,536-token context window with interleaved local-global attention
Apache-2.0 — unrestricted commercial use

Model Summary

Architecture	Rosetta dense decoder-only Transformer (`RosettaForCausalLM`)
Parameters	7B
Layers	32
Hidden size	4,096
Attention heads	32
Attention pattern	interleaved sliding-window (4,096) + global, 3:1, with QK-normalization
Context length	65,536
Vocabulary	161,425 (Korean-extended)
Post-training	SFT → preference optimization (DPO)
License	Apache-2.0

Training Overview

The Rosetta-7B family was built through a multi-stage pipeline carried out end-to-end by PoSTMEDIA:

Pretraining on trillions of tokens of curated bilingual web, code, and academic text
Staged mid-training for reasoning-dense data and long-context extension up to 65K
Korean continual pretraining on curated Korean corpora plus in-house synthetic Korean data assets, with the vocabulary extended to 161K for efficient Korean tokenization
Post-training — large-scale supervised fine-tuning followed by preference optimization (DPO) targeting instruction following, factuality, and safe refusals

Evaluation Results

All models in the table below, including competitors, were re-evaluated in-house under an identical protocol (lm-evaluation-harness + vLLM ≥ 0.26, identical prompts, decoding parameters, and generation budgets). Scores are therefore directly comparable within this table, but may differ from numbers reported elsewhere under different setups.

Benchmark	Rosetta-7B-Instruct ^7B	A.X-4.0-Light ^7B	A.X-3.1-Light ^7B	EXAONE-3.5 ^7.8B	Midm-2.0-Base ^11.5B	kanana-2 ^3B	Llama-3.1 ^8B	Ministral-3 ^8B
General & Reasoning
MMLU	60.2	73.0	57.7	73.4	68.0	59.5	66.6	72.4
GPQA	39.4	37.4	30.8	36.9	31.8	32.3	22.7	49.5
GSM8K	79.8	68.1	84.0	88.9	80.7	70.0	84.2	80.8
IFEval	80.4	82.8	77.3	82.1	81.3	76.5	80.0	63.8
Korean Language & Knowledge
KMMLU	45.6	58.1	49.9	52.0	56.4	44.4	42.7	48.5
CLIcK	59.2	68.1	53.0	66.2	70.7	58.2	54.7	55.0
HAE-RAE	63.0	74.0	67.0	73.7	79.2	70.8	55.3	56.8
HRM8K	60.4	46.4	51.1	49.8	51.2	31.4	30.8	54.2
KoSimpleQA^†	71.7	77.5	45.9	78.9	88.9	81.0	—	72.1

_{Bold indicates the best score in each row. — indicates not evaluated. ^† KoSimpleQA is evaluated as a judge-free 10-choice MCQA variant. Generative benchmarks use greedy decoding; subtask-style suites report the unweighted subtask mean.}

In the broadest same-protocol comparison we are aware of for Korean open models, Rosetta-7B-Instruct holds the top score on Korean mathematical reasoning (HRM8K 60.4) — ahead of every Korean flagship in the table — while placing in the leading group on instruction following (IFEval 80.4) and Korean factuality (KoSimpleQA 71.7). Models that lead on knowledge-recall benchmarks are either substantially larger (Midm-2.0-Base, 11.5B) or newest-generation flagships, and none of them match Rosetta on Korean math.

Quickstart

Transformers

Requires transformers>=5.13 and trust_remote_code=True (the Rosetta architecture ships as custom code in this repository).

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "PoSTMEDIA/Rosetta-7B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, dtype="bfloat16", device_map="auto", trust_remote_code=True
)

messages = [
    {"role": "user", "content": "한국의 전통 발효 음식 세 가지를 소개해줘."},
]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

out = model.generate(inputs, max_new_tokens=1024, temperature=0.7, top_p=0.9, do_sample=True)
print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

vLLM

Use the PoSTMEDIA vLLM distribution with native Rosetta support — no trust_remote_code required:

VLLM_USE_PRECOMPILED=1 pip install git+https://github.com/PoSTMEDIA-AI/vllm@rosetta-v0.26.0

vllm serve PoSTMEDIA/Rosetta-7B-Instruct --dtype bfloat16

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
resp = client.chat.completions.create(
    model="PoSTMEDIA/Rosetta-7B-Instruct",
    messages=[{"role": "user", "content": "부산 여행 1박 2일 코스를 짜줘."}],
    temperature=0.7,
)
print(resp.choices[0].message.content)

vLLM v0.26 or later is required. Recommended sampling: temperature 0.7, top_p 0.9 (or greedy for deterministic tasks).

Limitations

The model can generate factually incorrect or outdated information; verify outputs for high-stakes use.
Outputs may reflect biases present in web-scale training data.
Optimized for Korean and English; other languages are not guaranteed.
Alignment was performed on contexts up to 32K tokens; validate quality for longer inputs.

License

Apache License 2.0 — see LICENSE. If you build something with Rosetta, we'd appreciate a "Built with Rosetta" attribution.

Citation

@misc{rosetta2026,
  title  = {Rosetta-7B: A Bilingual Korean-English Language Model Family},
  author = {{PoSTMEDIA AI Lab}},
  year   = {2026},
  url    = {https://huggingface.co/collections/PoSTMEDIA/rosetta-6a9db30fd1b4585b0c1845e9}
}