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 /verify-extraction.json

Baekpica

Publish Swift1.5 Q5 with FP8 PLE

ae48755 verified 3 days ago

Raw History Blame Contribute Delete

6.19 kB

	{
	"elapsed_seconds": 111.13494417257607,
	"logical_parts_checked": 128,
	"manifest_sha256": "507885ac42e4a5f631a12cf95d12755980a4c61f3fc09b17f665be2a4af9312e",
	"ok": true,
	"physical_files_reread": 4,
	"requantized": false,
	"scale_bytes_preserved": true,
	"source": {
	"repo": "Qwen/Qwen3.8-Flash-Next-FP8",
	"revision": "236dfdf285828023ca3bcd3f37366c58a3469b13"
	},
	"source_dtype": "F8_E4M3",
	"source_files_verified_against_hub_sha256": [
	{
	"path": "model-00005-of-00131.safetensors",
	"sha256": "c50bf465a4a0129f1a1196c0e75d881048238f7107b356fc734007eac0d3b123",
	"size": 1717267690
	},
	{
	"path": "model-00006-of-00131.safetensors",
	"sha256": "57f103367fe36c9dc355c648d521d026caabb4ce5ba51038c78bd935f6593736",
	"size": 1600008328
	},
	{
	"path": "model-00007-of-00131.safetensors",
	"sha256": "cde2a12854770c706a74a5c7b85ffc140e3d4dba93dd4bee1cd7aa07cc348c90",
	"size": 1600008328
	},
	{
	"path": "model-00008-of-00131.safetensors",
	"sha256": "98ae323b5a61abdcf58a0dc9db7eb1a5a748cebc1ef1932804a1411500c09d84",
	"size": 1600008328
	},
	{
	"path": "model-00009-of-00131.safetensors",
	"sha256": "70f4b66bca5c4c0b441cf4284d4ab311670e182bfc5ec51c7294a4054baeb20f",
	"size": 1600008328
	},
	{
	"path": "model-00010-of-00131.safetensors",
	"sha256": "6d5dfa1dfa9a6c482f100f6e75c1f5e8ac02391c62e844e53f4a53996baf2206",
	"size": 1600008328
	},
	{
	"path": "model-00011-of-00131.safetensors",
	"sha256": "de085adb563e530e7ad157220cd1a219be189a1db0fcc3f2507886a2d78bd4e1",
	"size": 1600008328
	},
	{
	"path": "model-00012-of-00131.safetensors",
	"sha256": "97dac7c366ba19858d78c01364054136449ccb3cf60768587461cbf0bb582c90",
	"size": 1600008328
	},
	{
	"path": "model-00013-of-00131.safetensors",
	"sha256": "467be10b473d25e0581f8fb1bc986916c8b9301272f72e7a05762484ffc080a9",
	"size": 1600008328
	},
	{
	"path": "model-00014-of-00131.safetensors",
	"sha256": "38def6e9bbe6f242c2f92858c3e9a2b356d74d0180e323677158016dbee32ae7",
	"size": 1600008328
	},
	{
	"path": "model-00015-of-00131.safetensors",
	"sha256": "90c6c665f1f819bc6350714652c8fb77064fec57832ef51b878a0cdf9f602236",
	"size": 1600008328
	},
	{
	"path": "model-00016-of-00131.safetensors",
	"sha256": "88185c2530641b37dcad80d37b505c5257035e87ad29fa86640aae4814903e4f",
	"size": 1600008328
	},
	{
	"path": "model-00017-of-00131.safetensors",
	"sha256": "42e3b96359116d7471a74cb7de08f6a4e9e173830d562f1f74ff09d1d253ad38",
	"size": 1600008328
	},
	{
	"path": "model-00018-of-00131.safetensors",
	"sha256": "040adc7270efca6498c6f6a07dd7da14925d20217fe6de67223d6856fb4f2ad7",
	"size": 1600008328
	},
	{
	"path": "model-00019-of-00131.safetensors",
	"sha256": "b8a831992deb1a694439e37b7136ff2e181457a8683bff65f1f4b99772cabe58",
	"size": 1600008328
	},
	{
	"path": "model-00020-of-00131.safetensors",
	"sha256": "9426112fbd49ef4f5e9dd38dc2f93b046602e6d92cff732d33b96413ff1b40dc",
	"size": 1600008328
	},
	{
	"path": "model-00021-of-00131.safetensors",
	"sha256": "fce8426665f749ef38c4fad33b612f892b728c0f74fe60cab0e43ece785b4e77",
	"size": 1600008328
	},
	{
	"path": "model-00022-of-00131.safetensors",
	"sha256": "a20dd415f280774321538e6b9e78fba9f8e218a9090eae52ea7172b85b9c5a6d",
	"size": 1600008328
	},
	{
	"path": "model-00023-of-00131.safetensors",
	"sha256": "4100591839471a8721d9cedb251554dcef5ebc1dfd96ea7dce8912dd1e9921b2",
	"size": 1600008328
	},
	{
	"path": "model-00024-of-00131.safetensors",
	"sha256": "bbd6d7a4b54b6a6032e15b7c2fc9d6526377f89992d61da8837ad059f3210b45",
	"size": 1600008328
	},
	{
	"path": "model-00025-of-00131.safetensors",
	"sha256": "f9fa7b04de155dd2be74b5693d5b36e5463dabf956b9be36f67d4aae6b8dd304",
	"size": 1600008328
	},
	{
	"path": "model-00026-of-00131.safetensors",
	"sha256": "193c6b9da2c6b69c34d8a14b65234871db272761e712c2e56bac3f13c2d0cbf7",
	"size": 1600008328
	},
	{
	"path": "model-00027-of-00131.safetensors",
	"sha256": "d7efa372552c07ee8783e27c4b8ebd12fc75333f92553861bec8c7c0155a03fe",
	"size": 1600008328
	},
	{
	"path": "model-00028-of-00131.safetensors",
	"sha256": "5fc13d084d62c06b928dfb388a1b36f5c27cd25bd3cbb68a0b21e1c4cfc68e6e",
	"size": 1600008328
	},
	{
	"path": "model-00029-of-00131.safetensors",
	"sha256": "a11f5a6360b349159575871db5c553c40c37be68382b82551a2c2372f15f2811",
	"size": 1600008328
	},
	{
	"path": "model-00030-of-00131.safetensors",
	"sha256": "cac780a675f87fc699435a5207cbecb30a887271b5514b60b7e45d9bebe0e1ec",
	"size": 1600008336
	},
	{
	"path": "model-00031-of-00131.safetensors",
	"sha256": "d390a872eb3928b9d7c0a720ced792cc111613812a163d9fd6c97fd35d633897",
	"size": 1600008336
	},
	{
	"path": "model-00032-of-00131.safetensors",
	"sha256": "3b36fe4e7215a12cdc6c7499aafde51cfbce51266785184b99d3838a6b37b2c8",
	"size": 1600008336
	},
	{
	"path": "model-00033-of-00131.safetensors",
	"sha256": "3fb0504f406c0c71ee40e9e158492973c5c35337889c81b974892b407620c64c",
	"size": 1600008336
	},
	{
	"path": "model-00034-of-00131.safetensors",
	"sha256": "6d989a812a8d8d9330c8c4d21d7be98a0114705ca3bd7834d43bb268bad6be08",
	"size": 1600008336
	},
	{
	"path": "model-00035-of-00131.safetensors",
	"sha256": "8e56ba0a714198bd07a2d8fe9d91fbf5c72601c1b98edf09b289a2538445cb75",
	"size": 1600008336
	},
	{
	"path": "model-00036-of-00131.safetensors",
	"sha256": "c23fd6f6cfa9acfa961ba26e7c9c0dd7b05782c5bbf8f55e2526119b5c516fd4",
	"size": 1600008336
	},
	{
	"path": "model-00037-of-00131.safetensors",
	"sha256": "b824a11bf460bd78c068e3d301e914a0bec2f243a6eea4679be7200d179248e7",
	"size": 942343240
	}
	],
	"source_shape": [
	2500012,
	160
	]
	}