|
|
๐ก๏ธ New from Orca — OrcaCyber Zero 1.0 Post-trained for cybersecurity: vulnerability analysis, vulnerability reproduction, red teaming & offensive-security research. 98.07% pass@1 on CyberGym Level 1 · 1M context · function calling · OpenAI-compatible API. |
High-fidelity 3-bit Qwen3.8 for long-horizon agents.
54 GB โ 12.3 GB ยท +0.02% PPL ยท 93.2% Top-1 Agreement ยท 0.031 KLD ยท 262K Context
OrcaRouter AI Gateway ยท X ยท Discord ยท GitHub ยท All Models
--- > ## 27B reasoning. 12.3 GB. > > **OrcaSAQ2 27B** compresses Qwen3.8-27B from a **54 GB BF16 checkpoint to 12.3 GB** while preserving extremely high fidelity to the original model. > > Built for: **long-horizon agents ยท coding ยท tool use ยท reasoning ยท stateful execution** OrcaSAQ2 is a proprietary **sensitivity-aware mixed-precision quantization system** developed by OrcaRouter and its research team behind. It is optimized around one goal: **Preserve as much useful model behavior as possible inside a practical GPU memory envelope.** The resulting checkpoint provides: - **77.2% smaller storage footprint** - only **+0.02% perplexity** versus BF16 - **93.2% token-level Top-1 agreement** - **0.031 mean KLD** - **262K context** - thinking mode - tool calling - MTP speculative decoding - production serving through **vLLM** --- ## At a glance | Metric | BF16 | OrcaSAQ2 | |---|---:|---:| | Checkpoint | 54 GB | **12.3 GB** | | Relative size | 100% | **22.8%** | | Storage reduction | โ | **77.2%** | | Decoder precision | 16-bit | **3.21 bpw avg.** | | Perplexity | 5.6468 | **5.6482** | | PPL delta | โ | **+0.02%** | | Top-1 agreement | 100% | **93.2%** | | Mean KLD | โ | **0.031** | | Context | 262K | **262K** |4.4ร smaller. +0.02% perplexity.
The point is not 3-bit.
The point is what survives at 3-bit.
--- ## BF16 fidelity All numbers below are measured using **these exact OrcaSAQ2 weights** against the BF16 reference through the same evaluation path. ### WikiText-2 **16,376 predicted tokens** | Build | Size | Decoder Bits | Mean KLD โ | Top-1 Agreement โ | PPL โ | |---|---:|---:|---:|---:|---:| | Qwen3.8-27B BF16 | 54 GB | 16 | โ | 100% | 5.6468 | | **OrcaSAQ2 27B** | **12.3 GB** | **3.21** | **0.031** | **93.2%** | **5.6482** | ### Perplexity ```text BF16 5.6468 โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ OrcaSAQ2 5.6482 โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ Delta: +0.02% ``` ### Top-1 agreement ```text OrcaSAQ2 vs BF16 โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ 93.2% ``` ### Model footprint ```text Qwen3.8-27B BF16 โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ 54.0 GB OrcaSAQ2 โโโโโโโโโโโ 12.3 GB ``` **77.2% smaller.** --- ## Long-horizon agents ### Low-bit fidelity matters more as the horizon grows. Short benchmarks can hide small degradation. Agents cannot. A small model error can change a tool call. That changes the environment state. The changed state affects every decision that follows. ```text Plan โ Act โ Observe โ Decide โ Recover โ Repeat โ ... โ Task Success ``` Across long trajectories, small errors can compound into large behavioral differences. That makes long-horizon execution an especially useful stress test for compressed reasoning models. OrcaSAQ2 performs **strongly on long-horizon workloads relative to models in its deployment and parameter class**, despite operating from a **12.3 GB checkpoint**. This makes it particularly suitable for: - coding agents - terminal agents - browser agents - computer-use agents - security agents - repository-scale tasks - multi-tool workflows - failure recovery - long-running stateful execution ### Why this matters Perplexity asks: > **How similar is the next-token distribution?** Long-horizon evaluation asks: > **Can the model still finish the job after many decisions?** For agent models, both matter. --- ## Long-horizon performance Agent benchmarks depend heavily on the surrounding scaffold, tools, reasoning budget, timeouts and execution environment. The results below are therefore shown as **public reference points, not direct apples-to-apples comparisons**. #### SWE-bench Verified | Model | Reported score | |---|---:| | Claude Sonnet 4.6 | 79.6 | | Claude Sonnet 4.5 | 77.2 | | Gemini 3 | 76.2 | | **OrcaSAQ2 27B** | **70.0** | | Qwen3-Coder-480B-A35B | 69.6 | | Gemini 2.5 Pro | 63.8 | | GPT-4.1 | 54.6 | **70.0% SWE-bench Verified from a 12.06 GB 27B checkpoint.** #### Terminal-Bench 2.1 | Model / Agent | Reported score | |---|---:| | Gemini 3.1 Pro / Terminus 2 | 70.7 | | Claude Opus 4.6 / Claude Code | 70.1 | | Claude Opus 4.6 / Terminus 2 | 63.8 | | Claude Sonnet 4.6 / Claude Code | 58.5 | | **OrcaSAQ2 27B** | **58.4** | | Gemini 3 Flash / Gemini CLI | 56.9 | | GPT-5.4 / Terminus 2 | 54.8 | | Claude Sonnet 4.6 / Terminus 2 | 51.5 | **58.4% Terminal-Bench 2.1 while fitting in ~12 GB of checkpoint storage.** > Public scores use different agent stacks and should not be interpreted as a > strict model-only ranking. --- ## Architecture | | | |---|---| | **Base model** | [`Qwen/Qwen3.8-27B`](https://huggingface.co/Qwen/Qwen3.8-27B) | | **Architecture** | `Qwen3_5ForCausalLM` | | **Layers** | 64 | | **Hidden size** | 5120 | | **Hybrid attention** | 48 Gated DeltaNet + 16 full-attention layers | | **Context** | **262,144 tokens** | | **Vocabulary** | 248,320 | | **MTP head** | Included | | **Thinking** | Supported | | **Tool calling** | Supported | | **Checkpoint** | **12.3 GB** | | **Decoder average** | **3.21 bpw** | | **Serving** | **vLLM** | | **Vision** | Not included | | **License** | Apache-2.0 | --- ## Production serving ### Up to 90.1 tok/s single-stream on a 16 GB GPU Measured under a **15.7 GiB GPU memory cap**. | Configuration | 1 Stream | 8 Streams | 16 Streams | KV Pool | |---|---:|---:|---:|---:| | vLLM ยท MTP off | 65.3 tok/s | **332 tok/s** | **333 tok/s** | 29,354 tok | | **vLLM ยท MTP on** | **90.1 tok/s** | 220 tok/s | 219 tok/s | 14,563 tok | ### MTP speculative decoding ```text Single-stream decode MTP off โโโโโโโโโโโโโโโโโโโโโโโโโโโโโ 65.3 tok/s MTP on โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ 90.1 tok/s ``` **+38% single-stream decode throughput** MTP trades additional compute and KV capacity for stronger interactive decode performance. It is particularly useful for: - coding assistants - interactive agents - terminal agents - tool-heavy applications - low-concurrency inference For highly batched workloads, benchmark both configurations. --- ## 27B on a 16 GB GPU OrcaSAQ2's checkpoint is **12.3 GB**. That makes deployment possible on hardware that cannot hold the original 54 GB BF16 checkpoint. ```text 16 GB GPU โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ โ โ OrcaSAQ2 weights 12.3 GB โ โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ โ โ โ โ Remaining ~3.7 GB โ โ โโโโโโโโโโ โ โ โ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ ``` Actual usable memory depends on: - vLLM overhead - KV-cache configuration - MTP - batch size - context length - CUDA graph configuration A practical starting point for a 16 GB GPU is approximately **32K interactive context**, then tune based on the workload. The model architecture supports up to **262K context**. --- ## Built for agents ### Long-horizon execution ```text plan โ act โ observe โ recover โ repeat ``` ### Coding Repository-scale generation, editing, testing and debugging. ### Tool use Structured workflows where action-selection quality matters. ### Reasoning Preserving the capabilities of the 27B base model under an aggressive deployment constraint. ### Single-GPU deployment A **12.3 GB** checkpoint designed around practical inference hardware. ### Production serving **vLLM + MTP + OpenAI-compatible APIs.** --- ## Generation samples One prompt each, first attempt. ### Pelican on a bicycle The standard SVG test, asked for as an animation. Chain over the chainring, cranks 180ยฐ out of phase, parallax background. Pure SMIL, no JavaScript. Used as generated.
--- ## Quickstart ### Install ```bash pip install -U vllm huggingface_hub pip install git+https://github.com/Continuum-AI-Corp/OrcaSAQ2-kernel ``` --- ### Download ```bash hf download orcarouter/OrcaSAQ2-27B \ --local-dir ./OrcaSAQ2-27B ``` --- ### Serve with vLLM ```bash vllm serve ./OrcaSAQ2-27B \ --served-model-name OrcaSAQ2-27B \ --max-model-len 262144 \ --reasoning-parser qwen3 \ --enable-auto-tool-choice \ --tool-call-parser qwen3_xml \ --speculative-config '{"method":"qwen3_next_mtp","num_speculative_tokens":2}' ``` --- ## OpenAI-compatible API ```python from openai import OpenAI client = OpenAI( base_url="http://localhost:8000/v1", api_key="not-needed", ) response = client.chat.completions.create( model="OrcaSAQ2-27B", messages=[ { "role": "user", "content": "Analyze this repository and plan the next five actions." } ], ) print(response.choices[0].message.content) ``` --- ## Recommended sampling ```text temperature = 1.0 top_p = 0.95 top_k = 20 ``` Thinking mode is enabled by default. For agent deployments, benchmark against the actual tool schema, context distribution and reasoning budget used in production. --- ## Evaluation philosophy A low-bit reasoning model should not be judged by checkpoint size alone. We look at the intersection of:
Footprint ร BF16 Fidelity ร Capability ร Long-Horizon Stability ร Serving Performance
A useful low-bit model must remain useful after compression. --- ## Why perplexity alone is not enough Perplexity is useful and reproducible. It is not a complete measure of agentic capability. Quantization can affect: ```text reasoning โ planning โ tool selection โ state tracking โ recovery โ task completion ``` That is why OrcaSAQ2 reports BF16 fidelity metrics alongside downstream and long-horizon evaluation. --- ## Method OrcaSAQ2 uses a proprietary **sensitivity-aware mixed-precision quantization system** developed by OrcaRouter. The implementation is optimized to preserve model quality under a strict deployment-memory target. Detailed quantization methodology, calibration strategy, precision allocation and packing techniques are not currently disclosed. --- ## Limitations - OrcaSAQ2 inherits the capabilities, biases and limitations of Qwen3.8-27B. - Quantization is not mathematically lossless. - **93.2% Top-1 agreement** means some token decisions differ from BF16. - **+0.02% PPL** is a model-fidelity measurement and does not guarantee identical downstream performance. - Long-horizon comparisons should use a controlled same-harness evaluation. - This checkpoint is **text-only**. - The vision tower is not included. - OrcaSAQ2 requires the OrcaSAQ2 vLLM integration. - Maximum architectural context does not imply that the full context fits into every GPU memory envelope. --- ## Open source from OrcaRouter ### [OrcaCode Review](https://github.com/Continuum-AI-Corp/Orca-Code-Review) Open multi-model code review. ### [OrcaReplay](https://github.com/Continuum-AI-Corp/OrcaReplay) Record, replay, fork and debug AI-agent runs. ### [OrcaRouter Lite](https://github.com/Continuum-AI-Corp/OrcaRouter-Lite) Self-hosted multi-model AI infrastructure.Open model. Open harness. Open bill.
--- ## Citation ```bibtex @misc{qwen38, title = {Qwen3.8-Max: A New Bar for Coding and Cowork}, author = {{Qwen Team}}, year = {2026}, month = {August}, url = {https://qwen.ai/blog?id=qwen3.8} } ``` --- ## License **Apache-2.0** Inherited from: [`Qwen/Qwen3.8-27B`](https://huggingface.co/Qwen/Qwen3.8-27B) Quantization does not change the underlying license obligations. ---Route Smarter ยท Ship Safer ยท Spend Less
