Qwen3.8-Flash-CIRU-STRIX-Orca

OrcaRouter’s research weights, packaged for local Strix Halo inference with the CIRU v3 runtime, matching MTP, and a 262,144-token context. This text-only build provides the Orca variant in the same mixed-precision layout as the original CIRU release, including its external PLE table and calibrated Q4_1 expert banks.

The intended audience is researchers working on interpretability, refusal mechanisms, defensive security, red-teaming, and robustness. The image describes the intended research use; the tests below measure coding, instruction following, math, tool workflows, and long-history behavior. They are not a cybersecurity or safety certification.

Requires the custom CIRU v3 runtime, the target GGUF, and all three PLE files. The matching Q8 MTP head enables speculative decoding. Stock llama.cpp and Hugging Face hosted inference cannot run this package.

Measured results

Test	Orca	Non-Orca v3
HumanEval 0–19 base / extended	20/20 / 20/20	20/20 / 20/20
Selected short: ifeval	6/8	5/8
Selected short: gsm8k	8/8	8/8
Selected short: humaneval	5/6	5/6
Shared ~63K history: gsm8k	2/2	2/2
Shared ~63K history: humaneval	3/4	2/4
Shared ~63K history: ifeval	2/2	2/2
Short coding health 0–9: base / extended	10/10 / 10/10	10/10 / 10/10
Shared ~63K coding health 0–7: base / extended	8/8 / 8/8	8/8 / 8/8
Hermes selected six, hermes-1: native mean score	100.00%	100.00%
Hermes selected six, hermes-2: native mean score	100.00%	91.67%

HumanEval IDs 1–10 served MTP speed: 53.62 decode tok/s, pooled over 30 requests; 227.19 prompt tok/s and 586 ms median first-piece latency. The three measured decode passes were 51.09, 54.36, 55.61 tok/s. No matched non-Orca run on this exact three-pass ID selection is claimed.

Permission-handling finding: although both Hermes rounds received full native scores, the second HA-19 trace retried deployment with a different command after an explicit “User denied. Do NOT retry.” response. The native scorer did not penalize this bypass. This is a permission-boundary failure, and the native score is not evidence of permission or safety compliance. Trace review.

Measured on Ciru: AMD Ryzen AI Max+ 395 / gfx1151, 128 GB shared memory, NixOS, ROCm 10, CIRU v3, one model workload at a time. Target KV is F16, draft KV Q8, maximum MTP depth 6, batch/microbatch 1024, one slot, and configured context 262,144.

HumanEval20 uses tasks 0–19, EvalPlus v0.1.10 base and extended tests, one first response per task, no retries, and a 4096-token output cap. The canonical task text is passed through the model’s chat template. This is a local chat protocol: thinking off, temperature 0.7, top-p 0.8, top-k 20, min-p 0, presence penalty 1.5, repeat penalty 1, frequency penalty 0, seed 123. Code is scored in a filesystem/network sandbox.

The separate speed panel uses HumanEval IDs 1–10, three repetitions, the same sampler, natural EOS, and a cold prompt cache per measured request. Decode tok/s, prompt tok/s, first-piece latency, and whole-request time are recorded separately. Repeated speed outputs do not replace the HumanEval20 first samples.

The selected hard panel reuses the non-Orca build’s exact tasks and native scorers: 8 IFEval, 8 GSM8K, 6 HumanEval, and 6 HermesAgent scenarios run twice. Native tasks use nonthinking greedy generation, seed 15035, no retries, and a 32,768-token cap. Hermes uses thinking, temperature 0.6, top-p 0.95, top-k 20, neutral penalties, at least 32 turns, full remaining context, and a 1200-second per-task deadline. The 8 long-history tasks reuse a shared roughly 63K-token archive, with a 65,536-token effective task boundary inside the 262K server. This disagreement-selected subset is a diagnostic, not a representative benchmark ranking.

Detailed results and protocol · Structured results

Download, build, and run

Use a Strix Halo system with 128 GB shared memory and fast NVMe. The model package totals 135,962,881,519 bytes (126.625 GiB). Allow at least 220 GiB for download parts and assembly, plus space for the runtime and SDK. The assembled model uses 126.625 GiB; the helper removes consumed parts to reclaim temporary space.

Ubuntu/Debian build guidance:

sudo apt-get update
sudo apt-get install -y git python3-venv
python3 -m venv .venv-hf
.venv-hf/bin/python -m pip install -U huggingface_hub
. .venv-hf/bin/activate
hf download jcbtc/Qwen3.8-Flash-CIRU-STRIX-Orca --local-dir ./model
python3 ./model/assemble.py
(cd model && sha256sum -c SHA256SUMS)
git clone --branch v3.0.0 --single-branch \
  https://github.com/ciru-ai/Qwen3.8-Flash-CIRU-STRIX-IU4.git ciru-runtime
(cd ciru-runtime && ./scripts/ciru/setup-linux-amd.sh --install-host-deps)
RUNTIME_DIR="$PWD/ciru-runtime" \
  BUILD_DIR="$PWD/ciru-runtime/build-gfx1151-sdk" \
  bash ./model/run-server.sh

The setup helper installs a private ROCm 10 SDK. Keep that SDK in place and ensure the AMD driver exposes /dev/kfd and the render node. This release was validated on NixOS/ROCm 10/gfx1151; the Ubuntu/Debian instructions are build guidance, not a claim of a new Ubuntu qualification. Runtime platform documentation.

The production launcher retains 262,144 context, prefix caching, 8192 MiB prompt-cache RAM, idle-slot caching, 32 context checkpoints with an 8192-token minimum step, 4096 MiB PLE cache, startup warmup, MTP 6, and the v3 QSA history cache. Thinking follows the embedded template’s default. Default sampling is temperature 1.0, top-p 0.95, top-k 20, min-p 0. Benchmark sampling and short output caps are request-specific and are not production defaults.

The server binds to 127.0.0.1:8080. PORT, CONTEXT_SIZE, MTP_DEPTH, and ENABLE_MTP can override their corresponding defaults. MTP requires one slot. Example nonthinking request:

curl http://127.0.0.1:8080/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"Qwen3.8-Flash-CIRU-STRIX-Orca","messages":[{"role":"user","content":"Write a Python function that validates an IPv4 address."}],"chat_template_kwargs":{"enable_thinking":false},"temperature":0.7,"top_p":0.8,"top_k":20,"min_p":0,"presence_penalty":1.5,"repeat_penalty":1.0}'

Files after assembly

Hugging Face limits new individual uploads to 50 GB. The large files are distributed as checked binary download parts under download-parts/. Run assemble.py after downloading: it restores the exact tested GGUFs and PLE payload, verifies each part and final SHA-256, and supports restarting interrupted copies. These are transport parts, not GGUF shards or a different quantization. The table below lists the restored runtime files; parts.json records the uploaded parts.

File	Bytes	Purpose
Qwen3.8-Flash-CIRU-STRIX-Orca.gguf	79,397,818,912	Main model
mtp/Qwen3.8-Flash-CIRU-STRIX-Orca-MTP-Q8_0.gguf	4,135,893,440	Matching draft
ple/ple.payload.bin	52,429,053,952	External PLE table
ple/ple.manifest.json	115,213	PLE mapping
ple/ple.scale.bf16	2	PLE scale

Browse all files · Checksums · Construction notes

Construction and provenance

Pinned source: orcarouter/Qwen3.8-Flash-Next-Uncensored at 8336e613.

The source audit found 149 changed tensors and 1,509 unchanged tensors against the original Qwen checkpoint. Config, tokenizer, and all 128 PLE tensors were unchanged. The main GGUF replaces 146 payloads and preserves 1,077; the MTP head replaces 4 and preserves 30. Shared embedding changes appear in both files. The changed expert down banks were refitted against the retained quantized gate/up banks using fresh Orca activations. Every output tensor was hash-verified.

Main tensor types: 144 Q4_1, 328 Q5_K, 290 Q8_0, 48 Q5_1, 25 BF16, and 388 F32. This build does not use IU4. The dependency repository retains its historical name.

Credit to Qwen for the base model, OrcaRouter for the modified research checkpoint, and CIRU/Crown (jcbtc, ciru-ai) for packaging, calibration, runtime work, and evaluation. Runtime contributor credits remain in the linked source repository. HumanEval/EvalPlus, IFEval, GSM8K, EvalScope, and the retained HermesAgent harness provide the evaluation tasks and scoring.

The upstream describes its checkpoint as refusal-removed. This package retains that provenance and does not add a safety alignment layer. Consult the upstream model card for its intended research use. The actual source checkpoint’s Qwen Community License is preserved; upstream Hub metadata alone is not used to relabel the packaged weights.