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
```

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-Think	HuggingFace	Explicit reasoning (`<think>`)

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	—	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}
}

Contact

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

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Rosetta-7B: PoSTMEDIA's Korean-English 7B language model family — Base, Instruct, and Think (reasoning). • 3 items • Updated about 24 hours ago