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 /v3-followup-20260909 /HARD-PANEL.md

jcbtc

Add v3 task benchmark results and historical EvalScope wall times

b31a987 verified 2 days ago

preview code

Raw

History Blame Contribute Delete

9.17 kB

Ornith hard-panel comparison: CIRU v3 and Halo

All three builds completed the existing Ornith 1.5 difficulty panel with one request at a time, MTP enabled, and the same native tasks and graders. CIRU v3 took 25m 18.32s, compared with 29m 17.11s before and 24m 34.43s for Halo. Model load is shown separately.

V3 changed total wall time by -13.59% versus previous CIRU and +2.98% versus Halo. These are complete measured runs, including tool work, scoring, and harness overhead.

Build	Full wall after readiness	Load	Full wall including load	Total output tokens
Previous CIRU, MTP6	29m 17.11s	31.17s	29m 48.28s	33,294
CIRU v3, MTP6	25m 18.32s	31.15s	25m 49.47s	33,451
Halo Vulkan, MTP3	24m 34.43s	33.62s	25m 08.05s	31,058

Halo finished 43.89 seconds sooner than v3 in this run. V3 had faster prompt processing and long-history generation; Halo had faster short-task generation, fewer total output tokens, and one additional passing case in short IFEval and long HumanEval.

Hardware: Ciru, AMD Ryzen AI Max+395 / gfx1151, 128 GB unified memory, NixOS. The test uses the previously qualified one-slot profiles.

Correctness

Scores are kept separate by task family. The panel was selected from historical Ornith disagreements and failures; its rates do not estimate general model quality. One native sample per short/long task, exactly two Hermes rounds, no answer repair.

Build	Short IFEval strict	Short GSM8K	Short HumanEval	Long IFEval strict	Long GSM8K	Long HumanEval
Previous CIRU, MTP6	5/8	8/8	5/6	2/2	2/2	2/4
CIRU v3, MTP6	5/8	8/8	5/6	2/2	2/2	2/4
Halo Vulkan, MTP3	6/8	8/8	5/6	2/2	2/2	3/4

Build	Hermes native full passes / 12	Native mean points / 100	Reviewed end states / 12	Short health, base + extended	Long health, base + extended
Previous CIRU, MTP6	7/12	80.83	11/12	10/10 + 10/10	8/8 + 8/8
CIRU v3, MTP6	11/12	95.83	12/12	10/10 + 10/10	8/8 + 8/8
Halo Vulkan, MTP3	11/12	95.83	12/12	10/10 + 10/10	8/8 + 8/8

Health cases are easy regression sentinels; passing them does not imply high general coding quality. Native Hermes grades and reviewed task end states are distinct. Raw native outcome flags are retained in the data; the reviewed column annotates demonstrated grader artifacts without changing native scores. See the reviewed partials below.

Short-task speed

Build	Entire scored short stage	API request wall sum	Output tokens	New prompt tokens/s	Generated tokens/s
Previous CIRU, MTP6	320.33s	260.79s	8,911	269.15	38.44
CIRU v3, MTP6	320.85s	260.45s	9,311	350.34	39.09
Halo Vulkan, MTP3	275.60s	216.37s	8,255	272.61	43.97

Hermes agent speed and work

Build	Round	Wall	Output tokens	API calls	New prompt tokens/s	Generated tokens/s	Reviewed end states
Previous CIRU, MTP6	1	352.55s	6,933	34	285.20	35.21	6/6
Previous CIRU, MTP6	2	395.46s	8,963	37	264.48	35.62	5/6
CIRU v3, MTP6	1	381.56s	8,814	34	375.33	36.88	6/6
CIRU v3, MTP6	2	316.01s	7,246	33	364.35	37.45	6/6
Halo Vulkan, MTP3	1	307.74s	6,705	36	293.48	41.86	6/6
Halo Vulkan, MTP3	2	300.38s	7,380	35	282.75	41.78	6/6

A longer answer or extra tool work can increase wall time despite higher token throughput. Each round has fresh scenario state. The two repetitions expose variation; they do not establish a reliable failure probability.

Shared long history

Build	Seed prompt tokens	Seed wall	Seed prompt tokens/s	Long health wall	Long hard wall	Long hard output tokens	Long hard generation tokens/s
Previous CIRU, MTP6	63,000	206.33s	306.51	61.91s	283.92s	5,498	21.75
CIRU v3, MTP6	63,000	162.80s	388.50	40.67s	161.90s	5,090	35.93
Halo Vulkan, MTP3	63,000	201.00s	314.53	48.24s	202.96s	5,717	31.25

The same retained archive and task text were rendered with each served Qwen template/tokenizer. The physical server capacity is 262,144 tokens; the original long-workload boundary remains 65,536. Seeding is charged explicitly. These are returns to a shared history, not independently populated histories.

Case-level agent outcomes

Case	Previous R1 / R2	V3 R1 / R2	Halo R1 / R2
HA-02	100 / 50	100 / 100	100 / 50
HA-08	100 / 100	100 / 100	100 / 100
HA-10	100 / 100	100 / 100	100 / 100
HA-11	50 / 50	100 / 50	100 / 100
HA-12	100 / 100	100 / 100	100 / 100
HA-19	85 / 35	100 / 100	100 / 100

Reviewed partials and failures

Previous CIRU, MTP6, hermes-1, HA-11, native 50: The model used a broad edit despite the request for a narrow patch. The required skill content and Verification section survived. The native action penalty remains.
Previous CIRU, MTP6, hermes-1, HA-19, native 85: Deployment failed, was corrected, and succeeded within one shell tool call. The saved output and follow-up reads verify DEPLOY_OK and the release manifest. The native grader expects a later separate deployment call, so the 85-point result reflects command grouping. The native score remains unchanged.
Previous CIRU, MTP6, hermes-2, HA-19, native 35: The first attempt failed because deploy.sh lacked executable permission. The harness then explicitly denied the chmod-and-deploy request. The model respected that denial and correctly reported that deployment was incomplete. The task end state was not achieved; respecting the denial was appropriate.
Previous CIRU, MTP6, hermes-2, HA-02, native 50: All three facts were retained within the memory limit. The native grader at core.mjs:1010 only recognizes “not tag” or “not the tag”; the saved memory says “never tag”. This is a wording-matching artifact. The native score and outcome flag remain unchanged; the reviewed end state is successful.
CIRU v3, MTP6, hermes-2, HA-11, native 50: The model used a broad edit despite the request for a narrow patch. The required skill content and Verification section survived. The native action penalty remains.
Halo Vulkan, MTP3, hermes-2, HA-02, native 50: All three facts were retained within the memory limit. The native grader at core.mjs:1010 only recognizes “not tag” or “not the tag”; the saved memory says “never tag”. This is a wording-matching artifact. The native score and outcome flag remain unchanged; the reviewed end state is successful.

Reproducibility and limits

Runtime/model source identities, exact launch commands, samplers, templates, prompts, reasoning, native responses, tool traces, scoring decisions, and timings are retained with each run.
Previous CIRU and v3 use the same weights. Their runtime and batch/ubatch profiles differ; this measures the complete promoted change. Halo uses its unchanged Vulkan source and published compatible Q8 head. Its larger Q4_K_XL model makes the external result a serving-package comparison.
MTP depth is distinct from slot count: previous CIRU and v3 use depth 6; Halo uses depth 3. Every run has one server slot.
Native short tasks retain temperature 0, seed 15035, no thinking, and a 32768-token output allowance. Hermes retains temperature 0.6, top-p 0.95, top-k 20, thinking enabled and full remaining context. Full settings are in protocol locks and actual requests.
Native PP is newly processed prompt tokens divided by prompt phase seconds. TG follows both runtimes’ native convention: sum(output tokens minus one per request) divided by decode phase seconds. Wall throughput includes additional work and is separate.
Short quality was split into a scored three-case smoke and the remaining nineteen cases. Each is counted once. Scorer startup/cleanup is included equally for all builds.
Interrupted clarification captures and the canceled concurrency launch are preserved separately and excluded from this completed comparison. No reserve tasks or Ornith-specific BF16 token probes were consumed.
Hermes SSE timing fields were normalized from the preserved raw responses after capture; original recorder summaries are retained. This required no new inference.

The original v3 release passed its existing gates before publication. This additional hard-panel test was requested afterward. Published v3 artifacts remain unchanged.

GitHub v3 release · Hugging Face v3

Evidence checks passed: 81 new official speed rows, 24 official quality rows and 2,972 archived files verified by SHA-256. Raw evidence remains retained by the lab.

Public structured results · Research page