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

Libraries

How to use dinerburger/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="dinerburger/Qwen3.5-35B-A3B-GGUF",
	filename="Qwen3.5-35B-A3B.IQ4_NL.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use dinerburger/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 dinerburger/Qwen3.5-35B-A3B-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama-cli -hf dinerburger/Qwen3.5-35B-A3B-GGUF:IQ4_NL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dinerburger/Qwen3.5-35B-A3B-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama-cli -hf dinerburger/Qwen3.5-35B-A3B-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 dinerburger/Qwen3.5-35B-A3B-GGUF:IQ4_NL
# Run inference directly in the terminal:
./llama-cli -hf dinerburger/Qwen3.5-35B-A3B-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 dinerburger/Qwen3.5-35B-A3B-GGUF:IQ4_NL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dinerburger/Qwen3.5-35B-A3B-GGUF:IQ4_NL

Use Docker

docker model run hf.co/dinerburger/Qwen3.5-35B-A3B-GGUF:IQ4_NL

LM Studio
Jan
Ollama
How to use dinerburger/Qwen3.5-35B-A3B-GGUF with Ollama:
```
ollama run hf.co/dinerburger/Qwen3.5-35B-A3B-GGUF:IQ4_NL
```

Unsloth Studio new

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

Pi new

How to use dinerburger/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 dinerburger/Qwen3.5-35B-A3B-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": "dinerburger/Qwen3.5-35B-A3B-GGUF:IQ4_NL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dinerburger/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 dinerburger/Qwen3.5-35B-A3B-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 dinerburger/Qwen3.5-35B-A3B-GGUF:IQ4_NL

Run Hermes

hermes

Docker Model Runner
How to use dinerburger/Qwen3.5-35B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/dinerburger/Qwen3.5-35B-A3B-GGUF:IQ4_NL
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dinerburger/Qwen3.5-35B-A3B-GGUF:IQ4_NL

Run and chat with the model

lemonade run user.Qwen3.5-35B-A3B-GGUF-IQ4_NL

List all available models

lemonade list

This is an IQ4_NL quantization of Qwen3.5-35B-A3B, using the unsloth imatrix data, but with the following special rules applied:

The embedding and output layers were kept in BF16
All SSM tensors were left in BF16
All attention tensors were left in BF16
Shared expert tensors were left in BF16
All other tensors use IQ4_NL

The full quantization script is here:

QUANT="IQ4_NL"
llama-quantize \
  --output-tensor-type bf16 \
  --token-embedding-type bf16 \
  --tensor-type attn_qkv=bf16 \
  --tensor-type attn_v=bf16 \
  --tensor-type attn_q=bf16 \
  --tensor-type attn_k=bf16 \
  --tensor-type attn_gate=bf16 \
  --tensor-type ssm_ba=bf16 \
  --tensor-type ssm_beta=bf16 \
  --tensor-type ssm_alpha=bf16 \
  --tensor-type ssm_out=bf16 \
  --tensor-type ffn_down_shexp=bf16 \
  --tensor-type ffn_gate_shexp=bf16 \
  --tensor-type ffn_up_shexp=bf16 \
  --imatrix Qwen3.5-35B-A3B-imatrix.gguf_file \
  Qwen3.5-35B-A3B.bf16.gguf \
  Qwen3.5-35B-A3B.${QUANT}.gguf \
  ${QUANT}

Downloads last month: 42

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

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Model tree for dinerburger/Qwen3.5-35B-A3B-GGUF

Base model

Qwen/Qwen3.5-35B-A3B-Base

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Qwen/Qwen3.5-35B-A3B

Quantized

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