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 /v2.0 /controlled-coding-42tps.json

jcbtc

Launch v2.0 with verified non-thinking coding, fidelity and prefill results

bb64bae verified 27 days ago

Raw History Blame

12.3 kB

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	"--spec-draft-p-min",
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	"--spec-draft-p-split",
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	],
	"scope": "Two retained candidate repetitions inside order-balanced graph comparison. Raw output tokens identical. Not a native-thinking HumanEval average or a competitor speedup.",
	"binary_hashes_match_current_16_of_16": true,
	"reasoning_mode": "Explicitly closed think block before generation; non-thinking coding probe",
	"prompt": "<\|im_start\|>user\nWrite a Python TTL LRU cache using collections.OrderedDict and time.monotonic. Implement get, put, capacity eviction, and lazy expiration. Include three concise unit tests. Explain the expiry and recency invariants.<\|im_end\|>\n<\|im_start\|>assistant\n<think>\n\n</think>\n\n"
	}