Instructions to use ukisai/Swift-1.5-Qwen3.8-27B-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-Qwen3.8-27B-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-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S
# Run inference directly in the terminal:
llama cli -hf ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S

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-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S
# Run inference directly in the terminal:
llama cli -hf ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S

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-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S
# Run inference directly in the terminal:
./llama-cli -hf ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S

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-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S

Use Docker

docker model run hf.co/ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S

LM Studio
Jan

vLLM

How to use ukisai/Swift-1.5-Qwen3.8-27B-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-Qwen3.8-27B-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-Qwen3.8-27B-GSQ-RCO-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S

Ollama
How to use ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF with Ollama:
```
ollama run hf.co/ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S
```
Unsloth Desktop

How to use ukisai/Swift-1.5-Qwen3.8-27B-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-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S

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-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF with Docker Model Runner:
```
docker model run hf.co/ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S
```

Lemonade

How to use ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S

Run and chat with the model

lemonade run user.Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF-IQ2_S

List all available models

lemonade list

Hermes Agent

How to use ukisai/Swift-1.5-Qwen3.8-27B-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-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S

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-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ukisai/Swift-1.5-Qwen3.8-27B-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-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S

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-Qwen3.8-27B-GSQ-RCO-GGUF:IQ2_S" \
  --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 Qwen3.8-27B · GSQ-RCO

Compact, mixed-precision GGUF quantizations of Swift 1.5 Qwen3.8-27B, with Swift-specific refinement using the per-tensor allocations from ISTA-DASLab's GSQ-RCO release.

Swift 1.5 builds on Swift 1.0 through post-training focused on long-horizon, agentic and coding tasks, improving overall performance while using fewer thinking tokens. See the original model card for the model's training approach and benchmark results. Those model-level benchmarks are separate from the quantization measurements below.

Swift 1.5 uses 58.5% fewer thinking tokens while scoring 0.35% higher than the base, for a 9.18× speed-up on several tasks.

Available quantizations

Each tier is a single GGUF file. Sizes are decimal GB; runtime memory also includes the context cache and compute buffers. Tier names denote mixed-precision allocation profiles, rather than a uniform type for every tensor.

Tier	Standard GGUF	With MTP head	Development KLD ↓
IQ2_XS	8.42 GB	8.77 GB	0.189979
IQ2_S	9.26 GB	9.61 GB	0.134751
IQ3_XXS	10.09 GB	10.44 GB	0.097774
IQ3_S	11.77 GB	12.12 GB	0.051265

The optional -mtp files retain the matching refined model tensors and add the MTP head. They require a runtime with support for this model's MTP implementation. The KLD results here were measured on the standard files; MTP decoding speed and quality have not been separately evaluated.

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

Evaluation

KLD measures the divergence of the quantized model's next-token distribution from Swift 1.5 BF16; lower is better. Development measurements use wiki.test.raw, 100 chunks and a 512-token context. Development data informed refinement and is not independent validation.

The held-out results below use C4 prose, CodeParrot code, GSM8K math text and multilingual mC4 text. Prose/code/math use 100 chunks each; German/French/Spanish/Chinese use 25 chunks each, all at context 512. These are distributional KLD measurements, not task accuracy or math benchmark scores.

Held-out text	IQ2_XS	IQ2_S	IQ3_XXS	IQ3_S
C4 prose	0.161594	0.107438	0.080238	0.041748
CodeParrot code	0.120884	0.084739	0.062546	0.035447
GSM8K math text	0.117467	0.096348	0.075964	0.043916
German	0.124482	0.091422	0.074645	0.035698
French	0.166785	0.113277	0.077791	0.046219
Spanish	0.082313	0.056393	0.039906	0.024186
Chinese	0.207063	0.129485	0.103141	0.052241

All four refined files improve KLD over their matched Swift starting quantizations on all seven reporting domains. Results are not uniformly better than the ISTA comparison quants: math-text KLD is 4.9–7.7% higher, and IQ3_S is higher on five of seven domains. ISTA comparisons measure each quant against its own corresponding BF16 model; they are not direct Swift-versus-Qwen task rankings or proof of equivalent capability.

The full result table includes the matched Swift starting quant, ISTA comparison and reported error estimates. Evaluation metadata binds results to the exact released identities. A lexical overlap screen was applied against calibration/development text; it does not establish semantic deduplication or prove absence of overfitting. These 512-token tests do not establish quality at 32K or longer contexts.

Usage

Use a llama.cpp build that supports Qwen3.8. Authenticate with an account granted access while this repository is private.

hf download ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_XXS.gguf --local-dir .

llama-server \
  -m Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_XXS.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 the context size to fit available memory. The example context setting is not a claim that these quants were evaluated at that length.

This release provides the language-model GGUFs. A Swift 27B vision projector has not been verified for this release, so no projector or validated vision example is included.

Quantization procedure

Reuse the published ISTA GSQ-RCO per-tensor allocation for each matching Qwen3.8-27B tier.
Quantize Swift 1.5 weights with the Swift V1MIX importance matrix and the selected allocation.
Apply Swift-specific GSQ refinement. IQ2_XS and IQ3_XXS use the preserved fixed-objective variants; IQ2_S and IQ3_S use their preserved V1MIX variants.
Freeze exact file identities, run the reporting evaluations, and preserve the matching MTP packages.

This release reuses ISTA's allocation search results; it does not claim a new RCO search on Swift. The archived recipe records document construction settings and historical candidates; the release manifest identifies the selected files. The Swift importance matrix is included as imatrix-swift15-v1mix.gguf.

Per-tensor assignments for all eight files are included in tensor-allocation. Each dump records the model SHA256, tensor count and type histogram. MTP dumps contain the 851 model tensors plus 15 head tensors.

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, ISTA-DASLab for the quantization methods and published allocations, and NVIDIA Innovation Lab, AWS and Google Cloud for supporting Swift development.

License and access

These adapted weights are distributed under the Swift Open License v1.0. The original Qwen components retain their Apache 2.0 license and NOTICE. See the Swift license for applicable terms and enterprise licensing information.