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"

Website • Learn more • BF16 model • Standard GGUFs • Evaluation • Enterprise licensing

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

Mixed-precision GGUF quantizations of Swift Flash Next, with Swift-specific GSQ refinement and reused ISTA GSQ-RCO per-tensor allocation profiles.

Swift Flash Next is UkisAI's reasoning-efficient derivative of Qwen3.8-Flash-Next. Its post-training targets shorter reasoning traces and coding, agentic and long-horizon tasks. See the original model card for model-level benchmarks and training details. Those benchmarks are separate from the quantization measurements below.

Swift 1.5 Flash-Next uses 63.4% fewer thinking tokens, with a 1.8x speed up while keeping the accuracy loss <1% vs base on xhigh.

Available quantizations

Each tier contains two GGUF shards. Download both files into the same directory and load shard 1; llama.cpp locates shard 2 automatically. Sizes are decimal GB and exclude runtime context/cache memory. Tier names describe mixed-precision allocation profiles.

Tier	Combined GGUF size	Shards	Development KLD ↓
IQ3_XXS	75.97 GB	1 · 2	0.240139
IQ2_XS	68.15 GB	1 · 2	0.341275
Q2_0 (experimental)	66.55 GB	1 · 2	0.424350

A BF16 vision projector is included separately (0.91 GB). The evaluation below covers text inference; it does not measure vision-task accuracy.

Exact model and shard identities are recorded in release-manifest.json and SHA256SUMS.

Original evaluated, unsplit GGUF files can also be reconstructed byte for byte using the exact-source recovery files and script. These small files preserve headers and padding; normal inference needs only the two model shards. All three reconstructed source hashes were verified before release.

Evaluation

KLD measures divergence from the corresponding BF16 model's next-token distribution; lower is better. Swift quants are measured against Swift Flash Next BF16. Development measurements use 100 chunks at a 512-token context, and informed refinement.

Reporting prose, code and math sets use 100 chunks each; German, French, Spanish and Chinese use 25 chunks each, all at context 512. The seven original reporting sets were reused. IQ2_XS and Q2_0 additionally have results on a preregistered fresh English C4 shard; no matching fresh result is available for IQ3_XXS.

Reporting text	IQ3_XXS	IQ2_XS	Q2_0 (experimental)
English prose	0.116077	0.188117	0.234242
Fresh English sample	—	0.186271	0.236228
CodeParrot code	0.118707	0.174294	0.235256
GSM8K math text	0.086821	0.120991	0.149809
German	0.109100	0.166814	0.219444
French	0.133624	0.213379	0.300467
Spanish	0.073681	0.119114	0.148757
Chinese	0.174102	0.264923	0.385924

IQ2_XS is the standout. It has lower KLD than ISTA-DASLab's own GSQ-RCO IQ2_XS on seven of eight reporting sets, by 5–11% (math text −8.9%, Chinese −10.5%), and improves on its Swift starting quant in every domain, by 8–17%. IQ3_XXS improves on its Swift starting quant in six of seven domains, by 3–16%, and has lower KLD than ISTA's IQ3_XXS on math text (−7.8%) and Chinese (−5.1%); the other domains are within 1–4%.

Q2_0 is experimental. It improves on its Swift starting quant in five of seven domains, but has higher KLD than ISTA's Q2_0 on six of eight reporting sets. For a file of similar size, prefer IQ2_XS.

These comparisons use each model's own BF16 reference. They are not direct capability rankings, task-accuracy percentages or statistical-equivalence claims.

The full result table includes ISTA and starting-quant comparisons and reported error estimates. Evaluation metadata binds these results to the selected model identities and records limitations. Lexical overlap filtering does not prove semantic deduplication or absence of overfitting. These tests do not establish long-context quality.

Usage

Use a llama.cpp build supporting Qwen3.8-Flash-Next. Authenticate with an account granted access while the repository is private.

hf download ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF \
  --include "Swift-Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-*.gguf" --local-dir .

llama-server \
  -m Swift-Qwen3.8-Flash-Next-GSQ-RCO-IQ3_XXS-00001-of-00002.gguf \
  --jinja -fa on -ngl 99 -c 262144 \
  --temp 1.0 --top-p 0.95 --top-k 20 --min-p 0.0 \
  --presence-penalty 0.0 --repeat-penalty 1.0 --port 8000

Set context size and GPU offload to fit available memory. The example context setting is not a claim that these quantizations were evaluated at that length.

For image input, also download the projector and add --mmproj mmproj-Swift-Qwen3.8-Flash-Next-BF16.gguf to the server command:

hf download ukisai/Swift-1.5-Qwen3.8-Flash-Next-GSQ-RCO-GGUF mmproj-Swift-Qwen3.8-Flash-Next-BF16.gguf --local-dir .

Quantization procedure

Reuse the corresponding ISTA GSQ-RCO allocation profile to construct a Swift starting quant.
Apply Swift-specific refinement, including an attention/expert pass and a second expert pass, with native format checks.
Freeze the evaluated model identity and package its tensors into two GGUF shards.
Check that all 1,224 source tensors are present with no byte mismatches, and record shard hashes.

This release reuses allocation search results rather than claiming a new RCO search on Swift. The recipe summary identifies the selected variants and records the segmented-pass RNG limitation for IQ2_XS and Q2_0. The Swift V1MIX importance matrix and its provenance are included.

Per-tensor allocation dumps are provided in tensor-allocation, tied to the unsplit model identities in the release manifest.

Methods and acknowledgements

GSQ and RCO were developed by the Deep Algorithms and Systems Lab at the Institute of Science and Technology Austria. This Swift adaptation is by UkisAI.

GSQ: paper · code
RCO: paper · code
GGUF runtime and conversion: llama.cpp

We acknowledge the Qwen team for the original model and ISTA-DASLab for the quantization methods and published allocations.

License and access

The Swift contribution is distributed under the Swift Open License v1.0. The original Qwen components retain the Qwen Community License 1.0. See NOTICE and the license texts for applicable terms.