Instructions to use HuggingJoost/Swift-Qwen3.8-27B-NVFP4-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 HuggingJoost/Swift-Qwen3.8-27B-NVFP4-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 HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
llama cli -hf HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4

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 HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./llama-cli -hf HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4

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 HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4

Use Docker

docker model run hf.co/HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4

LM Studio
Jan

vLLM

How to use HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingJoost/Swift-Qwen3.8-27B-NVFP4-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": "HuggingJoost/Swift-Qwen3.8-27B-NVFP4-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/HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4

Ollama
How to use HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF with Ollama:
```
ollama run hf.co/HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4
```
Unsloth Desktop

How to use HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4

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": "HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4
```

Lemonade

How to use HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4

Run and chat with the model

lemonade run user.Swift-Qwen3.8-27B-NVFP4-GGUF-NVFP4

List all available models

lemonade list

Hermes Agent

How to use HuggingJoost/Swift-Qwen3.8-27B-NVFP4-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 HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4

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 HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4

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 "HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF:NVFP4" \
  --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-Qwen3.8-27B NVFP4 GGUF

A llama.cpp GGUF of ukisai/Swift-Qwen3.8-27B-NVFP4, UkisAI's calibrated NVFP4 checkpoint of Swift-Qwen3.8-27B (a reasoning-efficient fine-tune of Qwen3.8-27B). UkisAI publish that NVFP4 checkpoint for vLLM only; this file brings it to llama.cpp without requantizing any of its NVFP4 weights.

File	Size	BPW
`Swift-Qwen3.8-27B-NVFP4-Q8mix.gguf`	19.72 GB	5.77

Tensor layout

Tensors	Type	Source
All MLP (64 layers × gate/up/down) + full attention q/k/v/o (16 layers) — 256 tensors	NVFP4	UkisAI's llm-compressor NVFP4, byte-identical to the checkpoint (with per-tensor scales)
Gated DeltaNet linear attention (`attn_qkv`, `attn_gate`, `ssm_out`, 48 layers)	Q8_0	BF16 in the checkpoint
`output` (lm_head), `token_embd`	Q8_0	BF16 in the checkpoint
MTP head (`blk.64`, incl. `nextn.eh_proj`)	Q8_0	BF16 in the checkpoint
`ssm_alpha`, `ssm_beta`, `ssm_conv1d`	F32	BF16 in the checkpoint (lossless)

The MTP head is included, so --spec-type draft-mtp works.

How it was made

With the official ghcr.io/ggml-org/llama.cpp:full image (build b10975):

convert_hf_to_gguf.py --outtype bf16 on the checkpoint. It has a single nvfp4-pack-quantized config group, which the converter repacks natively as GGML NVFP4.
llama-quantize with an exact per-tensor --tensor-type-file: NVFP4 tensors map to nvfp4, which llama-quantize copies unchanged when source and target types match; the BF16 leftovers go to Q8_0, the recurrent gates to F32.
Verified: all 256 NVFP4 tensors and their 512 scale tensors are SHA-256 identical to the conversion of the checkpoint, and all 20 qwen35.* architecture metadata keys match UkisAI's own GGUF of Swift.

Usage

llama-server -m Swift-Qwen3.8-27B-NVFP4-Q8mix.gguf \
  --mmproj mmproj-Swift-Qwen3.8-27B-F16.gguf \
  --spec-type draft-mtp --spec-draft-n-max 3 \
  -c 262144 -fa on --jinja

NVFP4 runs natively on NVIDIA Blackwell. Other GPUs need a llama.cpp build with an NVFP4 fallback path.
Vision: use the projector from ukisai/Swift-Qwen3.8-27B-GGUF (mmproj-Swift-Qwen3.8-27B-F16.gguf).
Chat template: reasoning_effort accepts xhigh (default), medium, low. Unlike some Qwen templates, it does not accept high.
Sampling (stored in the GGUF header): temperature 1.0, top_p 0.95, top_k 20, min_p 0.

Quality

Not yet measured for this file. UkisAI report the source NVFP4 checkpoint within sampling noise of BF16 on their vLLM harness (MTP acceptance 61% vs 62%). For reference, UkisAI's own GGUF tiers measure KLD vs BF16 of 0.0120 (Q4_K_M), 0.0056 (Q5_K_M), 0.0020 (Q6_K), 0.0009 (Q8_0) on wikitext @512.

License

Swift weights are under the Swift Open License v1.0: free for personal, research, educational, evaluation and commercial use by individuals and organizations with annual recurring revenue (including affiliates) up to US$1,000,000. Above that, commercial use requires a Swift Enterprise License from UkisAI. See the source model card.

All credit for the model and the NVFP4 calibration goes to UkisAI; the base model is Qwen/Qwen3.8-27B by the Qwen team.

Downloads last month: 1,239

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for HuggingJoost/Swift-Qwen3.8-27B-NVFP4-GGUF

Base model

Qwen/Qwen3.8-27B

Finetuned

ukisai/Swift-Qwen3.8-27b

Quantized

ukisai/Swift-Qwen3.8-27B-NVFP4

Quantized

(2)

this model