Instructions to use ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF 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 ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS
# Run inference directly in the terminal:
llama cli -hf ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS
# Run inference directly in the terminal:
llama cli -hf ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

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 ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS
# Run inference directly in the terminal:
./llama-cli -hf ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

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 ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

Use Docker

docker model run hf.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

LM Studio
Jan

vLLM

How to use ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF",
		"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/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

Ollama
How to use ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF with Ollama:
```
ollama run hf.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS
```
Unsloth Desktop

How to use ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

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": "ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF with Docker Model Runner:
```
docker model run hf.co/ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS
```

Lemonade

How to use ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

Run and chat with the model

lemonade run user.Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF-IQ2_XS

List all available models

lemonade list

Hermes Agent

How to use ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

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 ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS

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 "ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF:IQ2_XS" \
  --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"

Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF

File size: 2,060 Bytes

b22d729

{
  "method": "Swift-specific GSQ refinement with reused ISTA GSQ-RCO per-tensor allocation profiles",
  "passes": [
    "Joint attention and expert refinement",
    "Second expert-refinement pass"
  ],
  "source_models": [
    {
      "tier": "IQ2_XS",
      "unsplit_sha256": "eb0dcf3c752d4b282eacc902e09200c1ac1fd974b4466e43f8a07699311f6580",
      "unsplit_bytes": 68152166976,
      "development_kld": 0.341275,
      "shards": [
        "Swift-Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00001-of-00002.gguf",
        "Swift-Qwen3.8-Flash-Next-GSQ-RCO-IQ2_XS-00002-of-00002.gguf"
      ],
      "tensor_count": 1224
    },
    {
      "tier": "Q2_0",
      "unsplit_sha256": "a14046f27675e53fadccaf99e8f7583efda6239d9932001ee927e3e33e710015",
      "unsplit_bytes": 66549952576,
      "development_kld": 0.42435,
      "shards": [
        "Swift-Qwen3.8-Flash-Next-GSQ-RCO-Q2_0-00001-of-00002.gguf",
        "Swift-Qwen3.8-Flash-Next-GSQ-RCO-Q2_0-00002-of-00002.gguf"
      ],
      "tensor_count": 1224
    },
    {
      "tier": "IQ3_XXS",
      "unsplit_sha256": "12e72a2fcb388b192c7352a1392770bfb60365a49b37875f146adf578c60e358",
      "unsplit_bytes": 75966072896,
      "development_kld": 0.240139,
      "shards": [
        "Swift-Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-00001-of-00002.gguf",
        "Swift-Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-00002-of-00002.gguf"
      ],
      "tensor_count": 1224
    }
  ],
  "importance_matrix": {
    "file": "imatrix-swiftfn-v1mix.gguf",
    "sha256": "769098d84e38baa285832e6335898cc5e04d578c8a65abbe4e164ce9795ac0fc"
  },
  "notes": [
    "Allocation reuse does not constitute a new RCO search on Swift.",
    "Native quantization-format support and validation applied for each tier.",
    "IQ2_XS and Q2_0 second passes were segmented, with prefix replay and a global torch RNG reset at the second segment; not bit-identical to an uninterrupted run.",
    "Q2_0 denotes a mixed per-tensor allocation profile, not uniform Q2_0 storage.",
    "This manifest identifies the exact evaluated and packaged candidates."
  ]
}