Instructions to use ubergarm/Qwen3.6-27B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ubergarm/Qwen3.6-27B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ubergarm/Qwen3.6-27B-GGUF",
	filename="Qwen3.6-27B-IQ4_KS.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ubergarm/Qwen3.6-27B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ubergarm/Qwen3.6-27B-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama-cli -hf ubergarm/Qwen3.6-27B-GGUF:IQ4_NL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ubergarm/Qwen3.6-27B-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama-cli -hf ubergarm/Qwen3.6-27B-GGUF:IQ4_NL

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/Qwen3.6-27B-GGUF:IQ4_NL
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/Qwen3.6-27B-GGUF:IQ4_NL

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/Qwen3.6-27B-GGUF:IQ4_NL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/Qwen3.6-27B-GGUF:IQ4_NL

Use Docker

docker model run hf.co/ubergarm/Qwen3.6-27B-GGUF:IQ4_NL

LM Studio
Jan

vLLM

How to use ubergarm/Qwen3.6-27B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/Qwen3.6-27B-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/Qwen3.6-27B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/Qwen3.6-27B-GGUF:IQ4_NL

Ollama
How to use ubergarm/Qwen3.6-27B-GGUF with Ollama:
```
ollama run hf.co/ubergarm/Qwen3.6-27B-GGUF:IQ4_NL
```

Unsloth Studio

How to use ubergarm/Qwen3.6-27B-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/Qwen3.6-27B-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/Qwen3.6-27B-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/Qwen3.6-27B-GGUF to start chatting

How to use ubergarm/Qwen3.6-27B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ubergarm/Qwen3.6-27B-GGUF:IQ4_NL

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/Qwen3.6-27B-GGUF:IQ4_NL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ubergarm/Qwen3.6-27B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ubergarm/Qwen3.6-27B-GGUF:IQ4_NL

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/Qwen3.6-27B-GGUF:IQ4_NL

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use ubergarm/Qwen3.6-27B-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/Qwen3.6-27B-GGUF:IQ4_NL
```

Lemonade

How to use ubergarm/Qwen3.6-27B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/Qwen3.6-27B-GGUF:IQ4_NL

Run and chat with the model

lemonade run user.Qwen3.6-27B-GGUF-IQ4_NL

List all available models

lemonade list

`ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3.6-27B

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 50.103 GiB (16.002 BPW)
- PPL over 580 chunks for n_ctx=512 = 6.9066 +/- 0.04552
Q8_0 26.622 GiB (8.502 BPW)
- PPL over 580 chunks for n_ctx=512 = 6.9063 +/- 0.04551

NOTE: If the models are split, the first file is much smaller and only contains metadata, that is on purpose, its fine!

IQ5_KS 18.532 GiB (5.919 BPW)

PPL over 580 chunks for n_ctx=512 = 6.9341 +/- 0.04578

This ik_llama.cpp exclusive quant is likely among the best quality available for 24GB full offload.

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 64 Repeating Layers [0-63]

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

# Dense Layers [0-63]
blk\..*\.ffn_down\.weight=iq5_ks
blk\..*\.ffn_(gate|up)\.weight=iq5_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 \
numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF/imatrix-Qwen3.6-27B-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF/Qwen3.6-27B-BF16-00001-of-00002.gguf \
    /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF/Qwen3.6-27B-IQ5_KS.gguf \
    IQ5_KS \
    128

smol-IQ4_NL 15.405 GiB (4.920 BPW)

PPL over 580 chunks for n_ctx=512 = 7.0040 +/- 0.04646

This mainline compatible custom mix using quantization types hopefully optimized for Vulkan/ROCm (and possibly Mac)?

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 64 Repeating Layers [0-63]

## Gated Attention/Delta Net [Blended 0-63]
blk\..*\.attn_gate\.weight=iq4_nl
blk\..*\.attn_qkv\.weight=iq4_nl
blk\..*\.attn_output\.weight=iq4_nl
blk\..*\.attn_q\.weight=iq4_nl
blk\..*\.attn_k\.weight=iq4_nl
blk\..*\.attn_v\.weight=iq4_nl
blk\..*\.ssm_alpha\.weight=q8_0
blk\..*\.ssm_beta\.weight=q8_0
blk\..*\.ssm_out\.weight=q8_0

# Dense Layers [0-63]
blk\..*\.ffn_down\.weight=iq4_nl
blk\..*\.ffn_(gate|up)\.weight=iq4_nl

# Non-Repeating Layers
token_embd\.weight=iq4_nl
output\.weight=q8_0
"

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

    #--dry-run \
numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF/imatrix-Qwen3.6-27B-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF/Qwen3.6-27B-BF16-00001-of-00002.gguf \
    /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF/Qwen3.6-27B-smol-IQ4_NL.gguf \
    IQ4_NL \
    128

MTP IQ4_KS 15.113 GiB (4.752 BPW)

(Should be the same as IQ4_KS without blk.64.* MTP tensors below)

Tested on ik_llama.cpp PR1736 with -mtp --draft-max 4 --draft-p-min 0.75

👈 Secret Recipe

#!/usr/bin/env bash

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

## MTP/nextn tensors
## No way to make imatrix data for blk.64.*
## Keep q8_0 for best MTP spec-decoding acceptance rate
## Adds ~430.41 MiB extra size over non-MTP quants
blk\.64\..*\.weight=q8_0

## 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=q6_0
blk\..*\.ssm_beta\.weight=q6_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 \
numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF/imatrix-Qwen3.6-27B-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF-mtp/Qwen3.6-27B-BF16-00001-of-00002.gguf \
    /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF-mtp/Qwen3.6-27B-MTP-smol-IQ4_KS.gguf \
    IQ4_KS \
    128
# renamed it to remove `-smol` before upload

IQ4_KS 14.693 GiB (4.693 BPW)

PPL over 580 chunks for n_ctx=512 = 6.9740 +/- 0.04599

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 64 Repeating Layers [0-63]

## 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=q6_0
blk\..*\.ssm_beta\.weight=q6_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 \
numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF/imatrix-Qwen3.6-27B-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF/Qwen3.6-27B-BF16-00001-of-00002.gguf \
    /mnt/data/models/ubergarm/Qwen3.6-27B-GGUF/Qwen3.6-27B-smol-IQ4_KS.gguf \
    IQ4_KS \
    128
# renamed it to remove `-smol` before upload

Quick Start

I can fit 128k context on 24GB VRAM with multimodal support and hit ~1400+ tok/sec prompt processing and ~90+ tok/sec generation with MTP depending on workload on a single 3090 Ti FE.

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/Qwen3.6-27B-GGUF/resolve/main/Qwen3.6-27B-MTP-IQ4_KS.gguf
model=/mnt/ai/models/ubergarm/Qwen3.6-27B-GGUF/Qwen3.6-27B-MTP-IQ4_KS.gguf
# wget https://huggingface.co/ggml-org/Qwen3.6-27B-GGUF/resolve/main/mmproj-Qwen3.6-27B-Q8_0.gguf
mmproj=/mnt/ai/models/ubergarm/Qwen3.6-27B-GGUF/mmproj-Qwen3.6-27B-Q8_0.gguf

SLOT_SAVE_DIR="/tmp/llama-slot-cache"
mkdir -p "$SLOT_SAVE_DIR"

CUDA_VISIBLE_DEVICES="0" \
./build/bin/llama-server \
  --model "$model" \
  --alias "Qwen3.6-27B" \
  -c 131072 \
  -ctk q8_0 -ctv q8_0 \
  -ctkd q8_0 -ctvd q8_0 \
  --merge-qkv \
  -muge \
  -ngl 99 \
  -t 1 \
  -tb 1 \
  -tm 16 \
  --host 127.0.0.1 \
  --port 8080 \
  --parallel 1 \
  --jinja \
  --ctx-checkpoints 32 \
  -cram 32768 \
  --spec-type mtp:n_max=4,p_min=0.0 \
  -mtprot iq4_ks \
  --no-mmproj-offload \
  --mmproj "$mmproj" \
  --slot-save-path "$SLOT_SAVE_DIR"

If you have multiple CUDA GPUs add -sm graph when running. This 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 can't spare the extra 640MiB of VRAM you can remove -mtprot iq4_ks which will slightly slow down generation.

References

Downloads last month: 10,477

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for ubergarm/Qwen3.6-27B-GGUF

Base model

Qwen/Qwen3.6-27B

Quantized

(455)

this model

Quantizations

1 model

ik_llama.cpp imatrix Quantizations of Qwen/Qwen3.6-27B

Big Thanks

Quant Collection

IQ5_KS 18.532 GiB (5.919 BPW)

smol-IQ4_NL 15.405 GiB (4.920 BPW)

MTP IQ4_KS 15.113 GiB (4.752 BPW)

IQ4_KS 14.693 GiB (4.693 BPW)

Quick Start

References

Model tree for ubergarm/Qwen3.6-27B-GGUF

`ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3.6-27B