Sabi-Noema

Sabi-Noema is an agentic model built on top of Qwen3.8-27B using exclusively open-source RL environments.

With Sabi-Noema, we simultaneously push the frontier of small model capability and reasoning efficiency.

⚡ Performance at a Glance

• Terminal-Bench 2.1: 76.0% (vs. 73.0% base)
• DeepSWE: 47.5% (vs. 38.5% base)
• τ³-bench: 93.2% (vs. 79.1% base) | 43.5% on banking (vs. 39.8% base)
• Hallucination Rate: 15.1% on AA-Omniscience (vs. 30.4% base)
• Token Efficiency: 2.3× fewer output tokens than Qwen3.8-27B and 2.0× fewer than Swift 1.5 on the Terminal-Bench tasks both solve

Resources

• Sabi-Noema-MLA — the same model with multi-head latent attention, for many long sessions per GPU

Benchmarks

Sabi-Noema against Qwen3.8-27B and comparison models. Same harnesses for every model: Terminal-Bench 2.1 following Artificial Analysis's settings on H100 TP2 deployments with up to 8 tasks per server (Muse Glimmer on TP1); τ³-bench (airline, retail, telecom) with a GPT-4.1 user simulator; DeepSWE uncapped (ThinkingCap: 3-hour cap per task; seeds with an infrastructure failure scored as a model fail are excluded); ClawEval graded by the harness's LLM judge. GPT-6 Luna (openai/gpt-6-luna) was called through OpenRouter at high reasoning effort; Muse Glimmer 30B was served locally in BF16 at its highest reasoning setting with its 131K-token context, and tasks that overflowed it count as failures. Qwen reports 73.0 on Terminal-Bench 2.1 with its own Terminus setup and 42.2 on DeepSWE 1.1 with the Claude Code harness. Terminal-Bench 2.1 is averaged over 2–3 runs per open model and DeepSWE over 2–3 seeds for Sabi-Noema, Sabi-Noema-MLA and Qwen3.8-27B; GPQA-Diamond, IFBench and AA-LCR over 3 runs where we ran them.

Efficiency

Sabi-Noema writes much less to get there. On the tasks both models solve, it uses less than half the output tokens of Qwen3.8-27B and about half those of Swift 1.5 on Terminal-Bench 2.1, and 29% fewer than Qwen3.8-27B on DeepSWE:

The Terminal-Bench token row in the benchmark table is a median over all tasks, which includes failed attempts that run long; comparing only tasks both models solve measures the same work.

Model Overview

Quickstart

Use the same generation parameters as Qwen3.8-27B; they are the defaults in generation_config.json.

A non-thinking mode (chat_template_kwargs={"enable_thinking": false}) is supported by the chat template but was not evaluated.

vLLM (one 80 GB GPU holds the ~55 GB of BF16 weights plus long contexts):

pip install "vllm==0.31.0"
vllm serve sabicap/sabi-noema --max-model-len 262144 --max-num-seqs 32 \
  --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml

--max-num-seqs 32: vLLM's default of 1024 exceeds the linear-attention state blocks available on one GPU. The evaluations above also used --default-chat-template-kwargs '{"preserve_thinking": false}' (earlier turns' reasoning is not replayed); examples/serve_vllm.sh runs the command with it.

Chat with any OpenAI client; tool calls use the Qwen3.8 XML format:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
tools = [{"type": "function", "function": {"name": "get_weather", "description": "Current weather for a city.",
          "parameters": {"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]}}}]
resp = client.chat.completions.create(
    model="sabicap/sabi-noema", messages=[{"role": "user", "content": "What's the weather in Paris?"}], tools=tools,
    temperature=1.0, top_p=0.95, max_tokens=32768, reasoning_effort="xhigh", extra_body={"top_k": 20})
msg = resp.choices[0].message
print(msg.reasoning, msg.tool_calls or msg.content)

examples/ has the full serve script, a thinking/multi-turn client and a complete tool round trip.

Transformers: AutoModelForImageTextToText.from_pretrained("sabicap/sabi-noema", dtype="bfloat16", device_map="auto") loads the full checkpoint; AutoModelForCausalLM loads the language model alone. See examples/transformers_generate.py.

llama.cpp: convert with convert_hf_to_gguf.py --no-mtp (the checkpoint has no MTP head) and serve with llama-server --jinja; see examples/llama_cpp.md.

Use Cases

• Long jobs in a shell or with tools, where a task takes many actions and has to be checked at the end
• Customer-facing agents bound by policy, including difficult customers, with correct tool calls over many turns
• Assistants that should say when they do not know: half Qwen3.8-27B's hallucination rate
• Cost-sensitive agentic serving: fewer output tokens per task than Qwen3.8-27B
• Many long sessions on one GPU: Sabi-Noema-MLA shrinks the attention cache from 64 KiB per token across the 16 full-attention layers to 18 KiB (3.6× smaller), or 9 KiB with an FP8 cache (7.1×), so many more agent sessions fit on the same card

Citation

@misc{sabi2026noema,
  title        = {Sabi-Noema},
  author       = {Seth, Aryan and Kharbanda, Siddhant and Banerjee, Atmadeep},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/sabicap/sabi-noema}},
  note         = {Agentic model built on Qwen3.8-27B with open-source RL environments}
}

Contact

contact@sabi.com