Instructions to use unsloth/Qwen3.5-35B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/Qwen3.5-35B-A3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="unsloth/Qwen3.5-35B-A3B-GGUF",
	filename="BF16/Qwen3.5-35B-A3B-BF16-00001-of-00002.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use unsloth/Qwen3.5-35B-A3B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-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 unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-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 unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL

LM Studio
Jan

vLLM

How to use unsloth/Qwen3.5-35B-A3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/Qwen3.5-35B-A3B-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": "unsloth/Qwen3.5-35B-A3B-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/unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL

Ollama
How to use unsloth/Qwen3.5-35B-A3B-GGUF with Ollama:
```
ollama run hf.co/unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
```

Unsloth Studio new

How to use unsloth/Qwen3.5-35B-A3B-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 unsloth/Qwen3.5-35B-A3B-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 unsloth/Qwen3.5-35B-A3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/Qwen3.5-35B-A3B-GGUF to start chatting

Pi new

How to use unsloth/Qwen3.5-35B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL

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": "unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use unsloth/Qwen3.5-35B-A3B-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 unsloth/Qwen3.5-35B-A3B-GGUF:UD-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 unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL

Run Hermes

hermes

Docker Model Runner
How to use unsloth/Qwen3.5-35B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
```

Lemonade

How to use unsloth/Qwen3.5-35B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.Qwen3.5-35B-A3B-GGUF-UD-Q4_K_XL

List all available models

lemonade list

March 3, 2026 updates ?

#25

by BitBuilder - opened Mar 4

Discussion

BitBuilder

Mar 4

@danielhanchen - You updated almost all of the GGUFs again today 3/3/26 but left out Qwen3.5-35B-A3B-UD-Q5_K_XL.gguf. It this intentional or an oversight? Also, what's changed today compared to the 2/27/26 uploads? Qwen 3.5's core models are great, but it would probably be better to take an extra day and have a higher quality release. Just a pattern on GGUF quants in the last 3-4 months from unsloth.

Separately, the deep dives into KLD, perplexity, accuracy across various quantizers is great stuff. Hopefully we see more such analysis for the more notable model quants from now on

BitBuilder

Mar 4

LankyFung

Mar 4

Q4_K_M is also disappeared

danielhanchen

Unsloth AI org Mar 4

Hey sorry - we're also doing 122B, 395B as well - 35B now reuses our calibration dataset since https://unsloth.ai/docs/models/qwen3.5/gguf-benchmarks used a generic imatrix process from another provider for fairer comparisons.

After careful more experimentation, our dataset is still better in many cases. We plan to provide some metrics soon!

Raul2718

Mar 4

@danielhanchen I noticed in your Qwen3.5 benchmarks (https://unsloth.ai/docs/models/qwen3.5/gguf-benchmarks) that quantizing certain layers like attn_qkv has a negative impact. I see the new GGUFs for the 35B model (e.g., Qwen3.5-35B-A3B-UD-Q4_K_XL.gguf) have been updated to reflect this.

However, some of the current 397B quants, like Qwen3.5-397B-A17B-UD-Q4_K_XL, still have these layers quantized at MXFP4. Are you planning to apply the same layer updates to the 397B reuploads as you did for the 35B models?

Tralalabs

Mar 4

*2026. March 3 2025 would be Qwen2.5.

BitBuilder changed discussion title from March 3, 2025 updates ? to March 3, 2026 updates ? Mar 5

BitBuilder

Mar 5

Yeah, 2026, not 2025 :| ... fixed title

danielhanchen

Unsloth AI org Mar 5

Hey folks please see https://www.reddit.com/r/LocalLLaMA/comments/1rlkptk/final_qwen35_unsloth_gguf_update/ which has all the details!

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment