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Rosetta-7B-Think
PoSTMEDIA/Rosetta-7B-Think
Rosetta-7B-Think is a text generation model from PoSTMEDIA. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
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At a glance
- Task
- Text Generation
- Library
- transformers
- License
- apache-2.0
- Model type
- rosetta
- Access
- Public
- Created
- Sep 8, 2026
- Updated
- Sep 11, 2026
- SHA
- d6f99bb4
Base models
Model card
From the Hugging Face model README
<div align="center">
Rosetta-7B-Think
Introduction
Rosetta-7B-Think is a 7B-parameter bilingual (Korean-English) reasoning model developed by PoSTMEDIA. Built on PoSTMEDIA's Rosetta dense decoder-only architecture and post-trained from Rosetta-7B-Base with large-scale supervised fine-tuning on long-form reasoning traces, it generates an explicit reasoning trace wrapped in <think> ... </think> before committing to a final answer.
Where most compact reasoning models concentrate their gains in English math, Rosetta-7B-Think was trained to reason in and about Korean: under our unified protocol it surpasses Qwen3-8B on Korean math reasoning (HRM8K) and Korean comprehension (HAE-RAE) — while critically, unlike several global reasoning models, it reliably terminates its reasoning on Korean inputs.
| Model | Download | Note |
|---|---|---|
| Rosetta-7B-Base | HuggingFace | Foundation model (completion-style) |
| Rosetta-7B-Instruct | HuggingFace | Instruction following / chat |
| Rosetta-7B-Instruct-NVFP4 | HuggingFace | NVFP4 4-bit of Instruct (NVIDIA Blackwell / DGX Spark) |
| Rosetta-7B-Think | HuggingFace | Explicit reasoning (<think>) (this model) |
| Rosetta-7B-Think-NVFP4 | HuggingFace | NVFP4 4-bit of Think (NVIDIA Blackwell / DGX Spark) |
Highlights
- Explicit reasoning — structured
<think>traces with reliable termination in both Korean and English - Korean math reasoning — HRM8K 64.3, above Qwen3-8B under the identical protocol
- Korean comprehension — HAE-RAE 62.7, above Qwen3-8B under the identical protocol
- Competition math from a 7B — AIME24 36.7 / AIME25 33.3, a large step up from same-size non-reasoning models
- Grade-school math — GSM8K 83.5 with fully traceable step-by-step reasoning
- 65,536-token context window — headroom for long reasoning traces
- Apache-2.0 — unrestricted commercial use
Model Summary
<div align="center"> <table><tbody> <tr><td><b>Architecture</b></td><td>Rosetta dense decoder-only Transformer (<code>RosettaForCausalLM</code>)</td></tr> <tr><td><b>Parameters</b></td><td>7B</td></tr> <tr><td><b>Layers</b></td><td>32</td></tr> <tr><td><b>Hidden size</b></td><td>4,096</td></tr> <tr><td><b>Attention heads</b></td><td>32</td></tr> <tr><td><b>Attention pattern</b></td><td>interleaved sliding-window (4,096) + global, 3:1, with QK-normalization</td></tr> <tr><td><b>Context length</b></td><td>65,536</td></tr> <tr><td><b>Vocabulary</b></td><td>161,425 (Korean-extended)</td></tr> <tr><td><b>Post-training</b></td><td>large-scale reasoning SFT (long-form reasoning traces)</td></tr> <tr><td><b>Reasoning format</b></td><td><code><think> ... </think></code> + final answer</td></tr> <tr><td><b>License</b></td><td>Apache-2.0</td></tr> </tbody></table> </div>Training Overview
- 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 (161K extended vocabulary)
- Reasoning post-training — large-scale SFT on long-form reasoning traces across math, code, science, and Korean-language tasks
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). Reasoning models are sampled at temperature 0.6, top-p 0.95 with a 32,768-token generation budget for competition math.
Rosetta-7B-Think holds the top score on Korean mathematical reasoning (HRM8K) in this comparison and beats Qwen3-8B on Korean comprehension (HAE-RAE), while being the smallest model in the table. Just as importantly, it terminates its reasoning reliably on Korean inputs — a failure mode that collapses the Korean scores of some global reasoning models under identical budgets.
Quickstart
Transformers
Requires transformers>=5.13 and trust_remote_code=True.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "PoSTMEDIA/Rosetta-7B-Think"
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": "127 × 43은 얼마인가요? 단계적으로 풀어주세요."}]
inputs = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
out = model.generate(inputs, max_new_tokens=4096, temperature=0.6, top_p=0.95, do_sample=True)
text = tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True)
# Split the reasoning trace from the final answer
if "</think>" in text:
reasoning, answer = text.split("</think>", 1)
reasoning = reasoning.replace("<think>", "").strip()
else:
reasoning, answer = "", text
print("REASONING:", reasoning[:500])
print("ANSWER:", answer.strip())
vLLM
Use the PoSTMEDIA vLLM distribution — native Rosetta support and a built-in reasoning parser:
VLLM_USE_PRECOMPILED=1 pip install git+https://github.com/PoSTMEDIA-AI/[email protected]
vllm serve PoSTMEDIA/Rosetta-7B-Think \
--dtype bfloat16 \
--reasoning-parser rosetta
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
resp = client.chat.completions.create(
model="PoSTMEDIA/Rosetta-7B-Think",
messages=[{"role": "user", "content": "소수가 무한히 많음을 증명해줘."}],
temperature=0.6,
top_p=0.95,
)
print("REASONING:", resp.choices[0].message.reasoning)
print("ANSWER:", resp.choices[0].message.content)
[!IMPORTANT] vLLM v0.26 or later is required. Recommended sampling:
temperature 0.6, top_p 0.95. Allow a generousmax_tokens(≥ 4,096; 32,768 for competition math) so reasoning traces can complete.
Limitations
- Reasoning traces increase latency and token usage; budget
max_tokensaccordingly. - The model can generate factually incorrect content inside fluent reasoning; verify high-stakes outputs.
- 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.
- Task
- Text Generation
- Library
- transformers
- Type
- rosetta
- License
- apache-2.0
- Languages
- ko, en
- Created
- Sep 8, 2026
- Updated
- Sep 11, 2026