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

Install from WinGet (Windows)

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

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

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

Use Docker

docker model run hf.co/ubergarm/Qwen3.8-27B-GGUF

LM Studio
Jan

vLLM

How to use ubergarm/Qwen3.8-27B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/Qwen3.8-27B-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": "ubergarm/Qwen3.8-27B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/Qwen3.8-27B-GGUF

Ollama
How to use ubergarm/Qwen3.8-27B-GGUF with Ollama:
```
ollama run hf.co/ubergarm/Qwen3.8-27B-GGUF
```

Unsloth Studio

How to use ubergarm/Qwen3.8-27B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ubergarm/Qwen3.8-27B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ubergarm/Qwen3.8-27B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ubergarm/Qwen3.8-27B-GGUF to start chatting

How to use ubergarm/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 ubergarm/Qwen3.8-27B-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ubergarm/Qwen3.8-27B-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.Qwen3.8-27B-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use ubergarm/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 ubergarm/Qwen3.8-27B-GGUF

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 ubergarm/Qwen3.8-27B-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ubergarm/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 ubergarm/Qwen3.8-27B-GGUF

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 "ubergarm/Qwen3.8-27B-GGUF" \
  --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"

`ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3.8-27B

NOTE ik_llama.cpp can also run your existing GGUFs from bartowski, unsloth, mradermacher, etc if you want to try it out before downloading my quants. Only a couple quants in this collection are compatible with mainline llamma.cpp/LMStudio/KoboldCPP/etc as mentioned in the specific description, all others require ik_llama.cpp.

Some of ik's new quants are supported with Nexesenex/croco.cpp fork of KoboldCPP with Windows builds. Also check for ik_llama.cpp windows builds by Thireus here..

These quants provide best in class perplexity for the given memory footprint.

Big Thanks

Shout out to Wendell and the Level1Techs crew, the community Forums, YouTube Channel! BIG thanks for providing BIG hardware expertise and access to run these experiments and make these great quants available to the community!!!

Also thanks to all the folks in the quantizing and inferencing community on BeaverAI Club Discord and on r/LocalLLaMA for tips and tricks helping each other run, test, and benchmark all the fun new models! Thanks to huggingface for hosting all these big quants!

Finally, I really appreciate the support from aifoundry.org so check out their open source RISC-V based solutions!

Quant Collection

Perplexity computed against wiki.test.raw. (lower is "better")

These two are just test quants for baseline perplexity comparison and not available for download here:

BF16 50.894 GiB (16.002 BPW)
- PPL over 580 chunks for n_ctx=512 = 6.9540 +/- 0.04500
Q8_0 27.042 GiB (8.502 BPW)
- PPL over 580 chunks for n_ctx=512 = 6.9554 +/- 0.04500

MTP IQ4_KS 15.749 GiB (4.731 BPW)

PPL over 580 chunks for n_ctx=512 = 6.9938 +/- 0.04527

New improved recipe! Size includes extra iq4_ks mtp head so no need for -mtprot iq4_ks now.

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 64 Repeating Layers [0-63] + blk.64 MTP/nextn tensors

## MTP/nextn tensors
blk\.64\..*\.weight=iq4_ks

## Gated Attention/Delta Net [Blended 0-63]
blk\..*\.attn_gate\.weight=iq4_ks
blk\..*\.attn_qkv\.weight=iq4_ks
blk\..*\.attn_output\.weight=iq4_ks
blk\..*\.attn_q\.weight=iq4_ks
blk\..*\.attn_k\.weight=iq4_ks
blk\..*\.attn_v\.weight=iq4_ks
blk\..*\.ssm_alpha\.weight=q8_0
blk\..*\.ssm_beta\.weight=q8_0
blk\..*\.ssm_out\.weight=q6_0

# Dense Layers [0-63]
blk\..*\.ffn_down\.weight=iq4_ks
blk\..*\.ffn_(gate|up)\.weight=iq4_ks

# Non-Repeating Layers
token_embd\.weight=q6_0
output\.weight=q8_0
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

    #--dry-run \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --extra-output-tensor iq4_ks \
    --imatrix /mnt/data/models/ubergarm/Qwen3.8-27B-GGUF/imatrix-Qwen3.8-27B-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3.8-27B-GGUF-mtp/Qwen3.8-27B-BF16-00001-of-00002.gguf \
    /mnt/data/models/ubergarm/Qwen3.8-27B-GGUF-mtp/Qwen3.8-27B-MTP-IQ4_KS.gguf \
    IQ4_KS \
    16

Quick Start

git clone https://github.com/ikawrakow/ik_llama.cpp.git
cd ik_llama.cpp

cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON -DGGML_CUDA_F16=ON
cmake --build build --config Release -j $(nproc)

# wget https://huggingface.co/ubergarm/Qwen3.8-27B-GGUF/resolve/main/Qwen3.8-27B-MTP-IQ4_KS.gguf
model=/mnt/ai/models/ubergarm/Qwen3.8-27B-GGUF/Qwen3.8-27B-MTP-IQ4_KS.gguf
# wget wget https://huggingface.co/ggml-org/Qwen3.8-27B-GGUF/resolve/main/mmproj-Qwen3.8-27B-Q8_0.gguf
mmproj=/mnt/ai/models/ubergarm/Qwen3.8-27B-GGUF/mmproj-Qwen3.8-27B-Q8_0.gguf

CUDA_VISIBLE_DEVICES="0" \
./build/bin/llama-server \
  --model "$model" \
  --alias "Qwen3.8-27B" \
  -c 131072 \
  -ctk q8_0 -ctv q8_0 \
  -ctkd q8_0 -ctvd q8_0 \
  --merge-qkv \
  -muge \
  -ngl 99 \
  -t 1 \
  -tb 1 \
  -tm 16 \
  --host 127.0.0.1 \
  --port 8080 \
  --parallel 1 \
  --jinja \
  --ctx-checkpoints 32 \
  -cram 32768 \
  --spec-type mtp:n_max=4,p_min=0.0 \
  --no-mmproj-offload \
  --mmproj "$mmproj" \

This command keeps the mmproj on CPU/RAM and is set to 16 physical cores currently, reduce -tm 8 etc if you have less cores.

If you have multiple CUDA GPUs add -sm graph.

References

Downloads last month: 3,467

GGUF

Model size

29B params

Architecture

qwen35

Hardware compatibility

Model tree for ubergarm/Qwen3.8-27B-GGUF

Base model

Qwen/Qwen3.8-27B

Quantized

(493)

this model

ik_llama.cpp imatrix Quantizations of Qwen/Qwen3.8-27B

Big Thanks

Quant Collection

MTP IQ4_KS 15.749 GiB (4.731 BPW)

Quick Start

References

Model tree for ubergarm/Qwen3.8-27B-GGUF

`ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3.8-27B