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: 2,489 Bytes

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tier	domain	kld	reported_error	chunks	context	ista_kld	starter_kld	delta_vs_ista_pct	delta_vs_starter_pct
IQ2_XS	prose_c4	0.188117	0.001955	100	512	0.200267	0.215863	-6.066900687582077	-12.853522836243359
IQ2_XS	code_codeparrot	0.174294	0.002979	100	512	0.184403	0.193624	-5.4820149346811125	-9.983266537206125
IQ2_XS	math_gsm8k	0.120991	0.002643	100	512	0.132848	0.146175	-8.925237865831626	-17.228664272276383
IQ2_XS	multilingual_mc4_de	0.166814	0.00467	25	512	0.158851	0.185316	5.012873699252762	-9.984027283127206
IQ2_XS	multilingual_mc4_fr	0.213379	0.005505	25	512	0.227306	0.241407	-6.126983009687381	-11.610268136383784
IQ2_XS	multilingual_mc4_es	0.119114	0.004094	25	512	0.126496	0.135836	-5.835757652415885	-12.310433169410185
IQ2_XS	multilingual_mc4_zh	0.264923	0.005239	25	512	0.296037	0.289135	-10.510172714897115	-8.373942967817783
IQ2_XS	fresh_c4_en_shard6	0.186271	0.001944	100	512	0.198573		-6.195202771776631	
Q2_0	code_codeparrot	0.235256	0.003868	100	512	0.219545	0.228021	7.156163884397282	3.1729533683301003
Q2_0	fresh_c4_en_shard6	0.236228	0.002652	100	512	0.218501		8.113006347797036	
Q2_0	math_gsm8k	0.149809	0.003194	100	512	0.163066	0.202867	-8.129836998515938	-26.15408124534794
Q2_0	multilingual_mc4_de	0.219444	0.00608	25	512	0.192854	0.248268	13.78763209474525	-11.610034317753392
Q2_0	multilingual_mc4_es	0.148757	0.004586	25	512	0.144779	0.171527	2.7476360521899057	-13.274878007544011
Q2_0	multilingual_mc4_fr	0.300467	0.007617	25	512	0.2717	0.337115	10.587780640412214	-10.871067736529083
Q2_0	multilingual_mc4_zh	0.385924	0.007076	25	512	0.446938	0.376247	-13.651557934210112	2.5719806403772028
Q2_0	prose_c4	0.234242	0.00257	100	512	0.218335	0.270309	7.285593239746269	-13.34287796558754
IQ3_XXS	prose_c4	0.116077	0.001601	100	512	0.114982	0.125842	0.9523229722913085	-7.7597304556507485
IQ3_XXS	code_codeparrot	0.118707	0.002266	100	512	0.117015	0.122811	1.4459684655813376	-3.3417202042162475
IQ3_XXS	math_gsm8k	0.086821	0.002149	100	512	0.094208	0.103453	-7.841159986413048	-16.076865823127417
IQ3_XXS	multilingual_mc4_de	0.1091	0.003632	25	512	0.104781	0.12331	4.12193050266747	-11.523801800340605
IQ3_XXS	multilingual_mc4_fr	0.133624	0.003993	25	512	0.130922	0.152166	2.063824261774183	-12.18537649672069
IQ3_XXS	multilingual_mc4_es	0.073681	0.002812	25	512	0.073858	0.080745	-0.23964905629721978	-8.748529320700971
IQ3_XXS	multilingual_mc4_zh	0.174102	0.004177	25	512	0.183406	0.169308	-5.072898378460899	2.831525976327187