Instructions to use SixVolts/Swift-Qwen3.8-27B-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 SixVolts/Swift-Qwen3.8-27B-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 SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL
# Run inference directly in the terminal:
llama cli -hf SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL

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 SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL

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 SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL

Use Docker

docker model run hf.co/SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL

LM Studio
Jan
Ollama
How to use SixVolts/Swift-Qwen3.8-27B-GGUF with Ollama:
```
ollama run hf.co/SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL
```
Unsloth Desktop

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL

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": "SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use SixVolts/Swift-Qwen3.8-27B-GGUF with Docker Model Runner:
```
docker model run hf.co/SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL

Run and chat with the model

lemonade run user.Swift-Qwen3.8-27B-GGUF-Q4_K_XL

List all available models

lemonade list

Hermes Agent

How to use SixVolts/Swift-Qwen3.8-27B-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 SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL

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 SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL

Run Hermes

hermes

Atomic Chat

OpenClaw

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL

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 "SixVolts/Swift-Qwen3.8-27B-GGUF:Q4_K_XL" \
  --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 GGUF for one Radeon PRO V620

A quantization of UkisAI's Swift-Qwen3.8-27B that fits one 32 GB GPU with 64k context, plus two speculative-decoding drafters for it. Built and measured on an AMD Radeon PRO V620 (Navi 21, gfx1030), a datacenter card that sells cheaply second-hand. The full setup (BIOS, kernel, ROCm, llama.cpp build, server, web tools) is in llama-navi21-furnace/deploy.

file	size	what it is
`Swift-Qwen3.8-27B-Q4_K_XL-noIQ.gguf`	16.6 GiB	the model
`dflash-Qwen3.8-27B-Q4_0-d2t64k-swiftxl.gguf`	1.3 GiB	DFlash2 block drafter
`mtp-Qwen3.8-27B-d2t64k-swiftxl.gguf`	1.0 GiB	multi-token-prediction (MTP) drafter

Use the llama.cpp fork sixvolts/llama-navi21-furnace, branch main, for the drafters: the MTP drafter's reduced vocabulary (below) and --spec-draft-temp are not in upstream llama.cpp. The model itself is a standard GGUF.

The model

Unsloth's UD-Q4_K_XL per-tensor recipe for Qwen3.8-27B, applied to Swift's F16 weights with Unsloth's importance matrix, except that the tensors Unsloth stores in IQ formats are stored as Q4_K (IQ formats dequantize slowly on gfx1030). Token embeddings Q4_K, output Q6_K.

KL divergence against Swift's own Q8_0, on held-out text:

file	size	KLD
this file	16.57 GiB	0.0092
Swift's Q4_K_M	16.79 GiB	0.0134

The drafters

A drafter proposes tokens and the model checks them in one batch, so the output is the model's own; the drafter only changes speed.

DFlash2: z-lab's Qwen3.8-27B-DFlash2 drafter, quantized from BF16 to Q4_0 (the fastest of the drafter formats tested on gfx1030).
MTP: Unsloth's MTP drafter for Qwen3.8-27B (MTP/mtp-Qwen3.8-27B-Q4_0.gguf). Swift did not retrain the MTP head, so the base model's works unchanged.
Reduced draft vocabulary (d2t64k): in both, the full 248k-token output head is replaced by the 65,536 most likely rows of Swift's output projection (this file's Q6_K rows, copied unchanged) and a map from those rows to token ids. The head is most of a drafter's cost, so this makes drafting several times cheaper; the kept set covers 98.9% of held-out model output, and tokens outside it simply cannot be drafted.

Speed on one V620

llama-server, warm, Swift sampling settings (temperature 1.0, top-p 0.95, top-k 20), with --spec-draft-temp 1.0 (drafts sampled from the drafter and checked with speculative sampling, which leaves the output distribution unchanged):

drafter	research chat	math / code / list / essay
none	24.3 t/s	about 24 t/s
MTP, 3 tokens	44.2 t/s	62 / 54 / 64 / 43 t/s
DFlash2, adaptive up to 7	42.3 to 43.5 t/s	76 / 54 / 69 / 40 t/s

At depth (decode t/s with the given number of tokens already in the context; temperature 1.0, a long-text summary task):

depth	none	MTP	DFlash2
4k	24.1	55.3	50.0
16k	23.0	53.0	55.3
40k	21.4	48.0	41.6
80k	19.3	47.2	39.2
120k	17.4	44.1	32.0

DFlash2 is the faster drafter for short mixed work; past about 30k tokens of context MTP is, because its cost does not grow with depth. Prefill (no drafter): 492 t/s at an empty context, 416 at 16k, 278 at 64k, 172 at 128k.

llama-server -m Swift-Qwen3.8-27B-Q4_K_XL-noIQ.gguf -ngl 99 -fa on -c 65536 \
  -md dflash-Qwen3.8-27B-Q4_0-d2t64k-swiftxl.gguf -ngld 99 \
  --spec-type draft-dflash --spec-draft-n-max 7 --spec-draft-temp 1.0 \
  --temp 1.0 --top-p 0.95 --top-k 20 --min-p 0

For MTP: -md mtp-Qwen3.8-27B-d2t64k-swiftxl.gguf --spec-type draft-mtp --spec-draft-n-max 3.

Reproducing

deploy/scripts/make-models.sh rebuilds all three files from the upstream sources; with the same llama.cpp build the results are byte-identical to these (SHA256SUMS).

License and changes

Swift-Qwen3.8-27B is UkisAI's fine-tune of Qwen3.8-27B (Copyright 2026 Alibaba Cloud, Apache License 2.0). UkisAI's contribution is licensed under the Swift Open License v1.0 (LICENSE): free for personal, research, educational and evaluation use, and for commercial use by individuals and organizations with gross annual revenue up to US$1,000,000; above that, commercial use requires a Swift Enterprise License from UkisAI. UkisAI's notices are in NOTICE; the Apache License 2.0 is in LICENSE-APACHE-2.0.

All three files contain Swift weights (the drafters contain rows of Swift's output projection), so all three are under the Swift Open License. The DFlash2 drafter body (z-lab) and the MTP drafter body (Unsloth) are Apache License 2.0.

Changes made here (Swift Open License section 4(b)):

Swift-Qwen3.8-27B-Q4_K_XL-noIQ.gguf: converted from UkisAI's Swift-Qwen3.8-27B-F16-*.gguf by quantization with llama.cpp llama-quantize, per-tensor types as described above, importance matrix imatrix_unsloth.gguf from unsloth/Qwen3.8-27B-GGUF. No retraining.
dflash-Qwen3.8-27B-Q4_0-d2t64k-swiftxl.gguf: z-lab's DFlash2 BF16 drafter quantized to Q4_0; output head replaced by 65,536 rows of the output projection of the Q4_K_XL file above, plus a d2t token map. No retraining.
mtp-Qwen3.8-27B-d2t64k-swiftxl.gguf: Unsloth's MTP drafter; output head replaced the same way. No retraining.

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GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

View +1 variant

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

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