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
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.
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)
[!IMPORTANT] 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.