Instructions to use Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-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 Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-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 Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16

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 Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16

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 Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16

Use Docker

docker model run hf.co/Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16

LM Studio
Jan

vLLM

How to use Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-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": "Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-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/Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16

Ollama
How to use Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF with Ollama:
```
ollama run hf.co/Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16
```
Unsloth Desktop

How to use Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16

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": "Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF with Docker Model Runner:
```
docker model run hf.co/Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16
```

Lemonade

How to use Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16

Run and chat with the model

lemonade run user.Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-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 Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16

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 Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16

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 "Baekpica/Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF:BF16" \
  --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"

Swift1.5-Qwen3.8-Flash-Next-Mixed-Quant-GGUF / PLE-FP8 /README.md

Baekpica

Publish Swift1.5 Q5 with FP8 PLE

ae48755 verified 3 days ago

preview code

Raw History Blame Contribute Delete

925 Bytes

Official Qwen FP8 PLE, reused for Swift1.5

These four files and the 2-byte BF16 scale were copied from the local Qwen FP8 sidecar after complete Swift/Qwen BF16 PLE identity was proven. The weights, scale and manifest are unchanged from the Qwen release.

Original source: Qwen/Qwen3.8-Flash-Next-FP8@236dfdf285828023ca3bcd3f37366c58a3469b13. Storage: FP8_E4M3FN, 128 logical parts, 160-byte rows, 4,096-byte alignment, shared BF16 multiplicative scale 0x3951.

Decode each E4M3FN code to BF16, multiply by the BF16 scale, and round to BF16. The runtime keeps compressed pages in a bounded cache.

verify-extraction.json and verify-independent.json are the original Qwen extraction receipts. hashes-independent.json records the fresh copied-file verification for this package. Run sha256sum -c SHA256SUMS from this directory.

See the root card and reproduction.