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"

jcbtc commited on 6 days ago

Commit

cf307d3

verified ·

1 Parent(s): 4f881d4

Identify missing QSA unified-cache fix; clarify smoke-test limits and split-KV guidance

Browse files

Files changed (2) hide show

README.md +1 -1
v2.0-checksums.sha256 +1 -1

README.md CHANGED Viewed

@@ -245,7 +245,7 @@ ENABLE_MTP=0 PARALLEL_SLOTS=2 CONTEXT_SIZE=524288 \
 With separate KV caches, `524288` is the total context allocation: **262144 per slot**, not 512K per agent. Inspect `/slots` for the actual limits.
-The released CIRU/ROCm 10 runtime passed 16 target-only requests in our 2026-09-06 two-slot smoke: eight with separate KV and eight with unified KV, including overlapping short/long and streamed requests. This does not validate filled 512K contexts or every agent workload. The older upstream fragmented-KV fix is already included. A separate proposed HIP host-buffer patch is being held because it changed the MTP coding continuation during regression testing; this update does not claim that HIP issue is fixed.
 [Two-slot instructions and upstream issue links](https://github.com/ciru-ai/Qwen3.8-Flash-CIRU-STRIX-IU4/blob/main/docs/RUNNING.md#parallel-requests-and-unified-kv-cache) · [Validation report](https://github.com/ciru-ai/Qwen3.8-Flash-CIRU-STRIX-IU4/blob/main/docs/PARALLEL_VALIDATION.md)

 With separate KV caches, `524288` is the total context allocation: **262144 per slot**, not 512K per agent. Inspect `/slots` for the actual limits.
+The released CIRU/ROCm 10 runtime passed 16 target-only requests in our 2026-09-06 two-slot smoke: eight with separate KV and eight with unified KV, including overlapping short/long and streamed requests. This does not validate filled 512K contexts or every agent workload. The community subsequently identified [#27994](https://github.com/ggml-org/llama.cpp/issues/27994), the Qwen3.8/QSA unified-cache sequence-isolation bug fixed upstream by [#27941](https://github.com/ggml-org/llama.cpp/pull/27941). **The relevant fix is missing from our v2.0 source. Keep `--no-kv-unified` explicit for multi-slot serving pending integration and validation.** The short marker tests do not validate conversation recall and cannot rule out that bug. The launcher guard addresses the separate one-slot MTP assertion. The HIP host-buffer candidate discussed in the validation report is a different issue and remains unshipped.
 [Two-slot instructions and upstream issue links](https://github.com/ciru-ai/Qwen3.8-Flash-CIRU-STRIX-IU4/blob/main/docs/RUNNING.md#parallel-requests-and-unified-kv-cache) · [Validation report](https://github.com/ciru-ai/Qwen3.8-Flash-CIRU-STRIX-IU4/blob/main/docs/PARALLEL_VALIDATION.md)

v2.0-checksums.sha256 CHANGED Viewed

@@ -1,5 +1,5 @@
 c16fda8809ad10f0d25d2243f4f83e76c29b80e0ee1622a4357a6f7235df97b4  .gitattributes
-f369a1ea3134d72f96dbd9290752fb860334aea20d961eaeb761732bff4be0aa  README.md
 3a46a7fd054305b2c1a931682015d93094312fb3b76c8ef83a2adb350b0f584c  assets/qwen38-flash-ciru-strix-iu4.jpg
 e260f72cba14e4c4e523794345f7de001444626240ab620d96dc44ee51beee90  assets/v2-bf16-fidelity.png
 88dd940fda0790ec6f474dc0a99019703114df253758ee14136c9686e93ea404  assets/v2-context-sweep.png

 c16fda8809ad10f0d25d2243f4f83e76c29b80e0ee1622a4357a6f7235df97b4  .gitattributes
+e72a8ff81f3bbab92860577f675d5339489c647c1d623800f64fed15ebc23bd2  README.md
 3a46a7fd054305b2c1a931682015d93094312fb3b76c8ef83a2adb350b0f584c  assets/qwen38-flash-ciru-strix-iu4.jpg
 e260f72cba14e4c4e523794345f7de001444626240ab620d96dc44ee51beee90  assets/v2-bf16-fidelity.png
 88dd940fda0790ec6f474dc0a99019703114df253758ee14136c9686e93ea404  assets/v2-context-sweep.png