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.

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

Training Overview

1. Pretraining on trillions of tokens of curated bilingual web, code, and academic text
2. Staged mid-training for reasoning-dense data and long-context extension up to 65K
3. Korean continual pretraining on curated Korean corpora plus in-house synthetic Korean data assets (161K extended vocabulary)
4. 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/vllm@rosetta-v0.26.0

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 generous max_tokens (≥ 4,096; 32,768 for competition math) so reasoning traces can complete.

Limitations

• Reasoning traces increase latency and token usage; budget max_tokens accordingly.
• 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.