Instructions to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0
# Run inference directly in the terminal:
llama cli -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0
# Run inference directly in the terminal:
llama cli -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0

Use Docker

docker model run hf.co/jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0

LM Studio
Jan

vLLM

How to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0

Ollama
How to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 with Ollama:
```
ollama run hf.co/jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0
```
Unsloth Desktop

How to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 with Docker Model Runner:
```
docker model run hf.co/jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0
```

Lemonade

How to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0

Run and chat with the model

lemonade run user.Qwen3.8-Flash-CIRU-STRIX-IU4-Q8_0

List all available models

lemonade list

Hermes Agent

How to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "jcbtc/Qwen3.8-Flash-CIRU-STRIX-IU4:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen3.8-Flash-CIRU-STRIX-IU4 / benchmarks /v4.4.0 /QUALIFICATION.md

jcbtc

Release v4.4: combined runtime, opt-in Boost and measured results

551722d verified 2 days ago

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v4.4 qualification

The exact final-all-v1-pm4 inference binaries and pinned HIP/ROCr were qualified on a 128 GB Strix Halo / gfx1151, NixOS, stock TheRock10 compiler/math. MTP3, F16 target/draft caches, one slot, 262144 context, batch/microbatch 8192, native production thinking for HA20. These are runtime changes; no model artifacts were changed.

Matched IU4 workload	v4.3 + S5/D0 control	v4.4 combined	Change
12960-token incident replay decode	37.79 tok/s	44.53 tok/s	+17.84%
Incident prefill	824.28 tok/s	889.13 tok/s	+7.87%
245760-token cold decode	7.62 tok/s	21.14 tok/s	+177.48%
245760-token prefill	751.15 tok/s	761.25 tok/s	+1.35%
Deep cold whole request	394.58 s	347.29 s	−11.98%
Cached append decode	7.51 tok/s	20.78 tok/s	+176.93%
Cached append whole request	69.73 s	25.84 s	−62.95%
Branch B revisit first piece	5.653 s	0.222 s	−96.08%

One seven-case final block per arm. The control already includes S5/D0 and is not the plain published v4.3 runtime. No mirrored confidence interval or universal speedup is claimed. Comparable generated texts and draft counters matched; branch A2 is excluded from cross-arm speed claims because the control changed its canonical output. Candidate peak system RAM was 1.275 GiB higher and GTT 91.94 MiB higher.

Numerical evidence: 20 full-vocabulary teacher-forced rows exact, 11 lifecycle rows exact, 11 wide-index oracle cases passed. GDN specialization dispatch was confirmed. These checks qualify the complete IU4 composition; not every included patch has an isolated positive serving effect.

IU4 HumanEval 0–9

Ten canonical prompts, one cold slot per task, greedy nonthinking native chat adapter, natural EOS, no output cap, seed 123. This is a speed panel, not a graded coding accuracy score. Allocated context is 262144; prompts are short.

Setting	Pooled timed decode	Generated tokens	Total request time
Default MTP3	60.351 tok/s	1633	36.057 s
Opt-in Boost + MTP3	64.067 tok/s	1633	33.864 s

Boost improved pooled decode 6.16% in this one follow-up load. Nine outputs were byte-identical; one differed only by quote style and had the same parsed Python AST. Peak per-task Boost decode was 70.95 tok/s (HE3). This does not qualify long thinking, HA20, or Orca with Boost. Dataset SHA256: b796127e635a67f93fb35c04f4cb03cf06f38c8072ee7cee8833d7bee06979ef.

IU4 Hermes Agent 20

One first trajectory per task, frozen 32-turn adapter, production native xhigh thinking, temperature 1, top-p .95, top-k 20, min-p 0, seed 160916, unlimited generation within context. Result: 19/20 full-score tasks; 98.5/100 arithmetic mean (matching the old card's aggregation), 99/100 canonical weighted score. HA17 scored 70 for the same delegation artifact issue seen in both older IU4 runs.

IU4 version / run	Arithmetic mean /100	Full-score tasks
Published v4.3, pass 1	95.0	17/20
Published v4.3, pass 2	98.5	19/20
v4.4, one pass	98.5	19/20

Same suite and arithmetic aggregation; historical engines/seeds differ. This comparison does not establish statistical equivalence, lower weight error or a causal quality gain. No new failed scenario appeared. Locked protocol SHA256: 6465f9465b78e9ca0b1456a8dfee2fd6df7e04681c7169dfefed9ebf9e025075. HermesAgent-20 57d7766bf3db8c40696e3ed937d43c8c85f4cd6c; Hermes agent ea74f61d983ebdfd6a863c45761d1b38081f1d08.

Capacity and limitations

A separate native target-only 524288-token prefill measured 604.651 tok/s (867.092 s), after an excluded 8192-token warmup, batch/microbatch16384, F16 cache. Minimum available RAM was 9.750 GiB. No 512K MTP, decode, or quality claim. New-build target-only decode was not measured.

Image+MTP returned the same pending-row error in both control and candidate. Target-only red-square/blue-circle fixtures passed. Keep vision target-only. Boost is optional and lacks a new HA20 run. Model weights/scales/PLE are unchanged, so weight reconstruction error is unchanged.

The shared package is released with IU4's completed qualification. Orca keeps its retained MTP4 default and receives a separate HE0–9 / HA20 release report after its requested checks. IU4 speed figures are not Orca measurements.