Instructions to use Pawellll/Qwen3.5-27B-IQ4_KS-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pawellll/Qwen3.5-27B-IQ4_KS-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Pawellll/Qwen3.5-27B-IQ4_KS-GGUF",
	filename="Qwen3.5-27B-IQ4_KS.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Pawellll/Qwen3.5-27B-IQ4_KS-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Pawellll/Qwen3.5-27B-IQ4_KS-GGUF
# Run inference directly in the terminal:
llama-cli -hf Pawellll/Qwen3.5-27B-IQ4_KS-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Pawellll/Qwen3.5-27B-IQ4_KS-GGUF
# Run inference directly in the terminal:
llama-cli -hf Pawellll/Qwen3.5-27B-IQ4_KS-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 Pawellll/Qwen3.5-27B-IQ4_KS-GGUF
# Run inference directly in the terminal:
./llama-cli -hf Pawellll/Qwen3.5-27B-IQ4_KS-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 Pawellll/Qwen3.5-27B-IQ4_KS-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Pawellll/Qwen3.5-27B-IQ4_KS-GGUF

Use Docker

docker model run hf.co/Pawellll/Qwen3.5-27B-IQ4_KS-GGUF

LM Studio
Jan
Ollama
How to use Pawellll/Qwen3.5-27B-IQ4_KS-GGUF with Ollama:
```
ollama run hf.co/Pawellll/Qwen3.5-27B-IQ4_KS-GGUF
```

Unsloth Studio

How to use Pawellll/Qwen3.5-27B-IQ4_KS-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 Pawellll/Qwen3.5-27B-IQ4_KS-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 Pawellll/Qwen3.5-27B-IQ4_KS-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Pawellll/Qwen3.5-27B-IQ4_KS-GGUF to start chatting

How to use Pawellll/Qwen3.5-27B-IQ4_KS-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Pawellll/Qwen3.5-27B-IQ4_KS-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": "Pawellll/Qwen3.5-27B-IQ4_KS-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Pawellll/Qwen3.5-27B-IQ4_KS-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 Pawellll/Qwen3.5-27B-IQ4_KS-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 Pawellll/Qwen3.5-27B-IQ4_KS-GGUF

Run Hermes

hermes

Docker Model Runner
How to use Pawellll/Qwen3.5-27B-IQ4_KS-GGUF with Docker Model Runner:
```
docker model run hf.co/Pawellll/Qwen3.5-27B-IQ4_KS-GGUF
```

Lemonade

How to use Pawellll/Qwen3.5-27B-IQ4_KS-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Pawellll/Qwen3.5-27B-IQ4_KS-GGUF

Run and chat with the model

lemonade run user.Qwen3.5-27B-IQ4_KS-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Qwen3.5-27B-IQ4_KS GGUF

IQ4_KS quantization of Qwen/Qwen3.5-27B using ik_llama.cpp.

	BF16	Q4_K_M	IQ4_KS
Size	51 GB	16 GB	14 GB
Bits per weight	16	4.5	4.25

Quantized with importance matrix (wikitext + c4, ~580k tokens). Output tensor Q6_K, token embeddings Q5_K.

Benchmarks (100 samples, 0-shot, no thinking)

Benchmark	BF16 (51 GB)	Q4_K_M (16 GB)	IQ4_KS (14 GB)
HellaSwag	92.0	92.0	91.0
ARC-Challenge	98.0	98.0	99.0
ARC-Easy	100	99.0	100
WinoGrande	81.0	80.0	78.0
BoolQ	91.0	92.0	92.0
OpenBookQA	98.0	98.0	97.0
COPA	99.0	99.0	99.0
SciQ	99.0	99.0	99.0
GSM8K	74.0	74.0	74.0
TruthfulQA MC1	87.0	86.0	87.0
MMLU	80.0	78.0	79.0
MMLU-Pro	50.0	52.0	56.0
GPQA Diamond	55.0	54.0	53.0
Average	84.9	84.7	84.9

KL Divergence vs BF16

Model	Size	RMS Δp	Same top p
BF16	51 GB	0.00%	100.00%
Q4_K_M	16 GB	3.03%	95.06%
IQ4_KS	14 GB	3.19%	94.80%

RMS Δp measures how much token probabilities shift compared to BF16. Same top p is how often the most likely token stays the same. IQ4_KS preserves 94.80% of top token predictions while being 3.6x smaller than BF16 and 2 GB smaller than Q4_K_M.

Usage

Requires ik_llama.cpp.

# -c: context size, -ngl: GPU layers (999=all), -fa: flash attention, --jinja: chat template
./llama-server -m Qwen3.5-27B-IQ4_KS.gguf -c 32768 -ngl 999 -fa 1 --jinja

Downloads last month: 39

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

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Qwen/Qwen3.5-27B

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