Instructions to use h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-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 h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-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 h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama cli -hf h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF:IQ4_NL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF:IQ4_NL
# Run inference directly in the terminal:
llama cli -hf h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-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 h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF:IQ4_NL
# Run inference directly in the terminal:
./llama-cli -hf h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-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 h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF:IQ4_NL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF:IQ4_NL

Use Docker

docker model run hf.co/h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF:IQ4_NL

LM Studio
Jan
Ollama
How to use h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF with Ollama:
```
ollama run hf.co/h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF:IQ4_NL
```

Unsloth Studio

How to use h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-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 h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-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 h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF to start chatting

How to use h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-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": "h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF:IQ4_NL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-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 h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-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 h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF:IQ4_NL

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF:IQ4_NL

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 "h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF:IQ4_NL" \
  --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"

Docker Model Runner
How to use h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF with Docker Model Runner:
```
docker model run hf.co/h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF:IQ4_NL
```

Lemonade

How to use h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull h4rm0n1c/qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF:IQ4_NL

Run and chat with the model

lemonade run user.qwen3.5-24b-a3b-reap-0.32-iq4_nl-GGUF-IQ4_NL

List all available models

lemonade list

h4rm0n1c commited on Jun 17

Commit

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+---
+license: apache-2.0
+library_name: gguf
+base_model: sandeshrajx/Qwen3.5-24B-A3B-REAP-0.32
+tags:
+- qwen3.5
+- moe
+- gguf
+- iq4_nl
+- reap
+- pruned
+- quantzhai
+- quantized
+---
+# Qwen3.5-24B-A3B-REAP-0.32 IQ4_NL GGUF
+GGUF quantization of [sandeshrajx/Qwen3.5-24B-A3B-REAP-0.32](https://huggingface.co/sandeshrajx/Qwen3.5-24B-A3B-REAP-0.32) — a REAP-pruned 24B A3B MoE model with **10B active parameters**.
+**REAP** (Razor Edge And Pruning, [arxiv:2510.13999](https://arxiv.org/abs/2510.13999)) is a structured pruning technique that reduces the base Qwen3.5 model while preserving capability.
+**Source:** [sandeshrajx/Qwen3.5-24B-A3B-REAP-0.32](https://huggingface.co/sandeshrajx/Qwen3.5-24B-A3B-REAP-0.32)
+**Converted by:** [QuantZhai](https://github.com/h4rm0n1c/quantzhai) benchmark pipeline
+**Quantization:** IQ4_NL with importance matrix (imatrix from sandeshrajx)
+## Model Details
+| Property | Value |
+|---|---|
+| Architecture | Qwen3.5 MoE, REAP-pruned |
+| Parameters | 24B total, ~10B active |
+| Blocks | 40 |
+| Experts | 175 (REAP-split), 8 active per token |
+| Context length | 262144 (256K) |
+| Hidden size | 3072 |
+| Attention heads | 32, KV heads = 2 |
+| Quantization | IQ4_NL (4.58 bpw) with imatrix |
+| File size | 14.0 GB |
+## Benchmarks
+Hardware: dual-GPU (RTX 3080 10GB + V100-SXM2 32GB)
+Engine: llama.cpp with TurboQuant KV (q8_0 K / turbo3 V)
+Perplexity: [`macvox68`](https://github.com/h4rm0n1c/macvox68) code corpus, ctx=4096, stride=512
+| Metric | Cold | Warm |
+|---|---|---|
+| PPL | 3.0205 | **1.8298** |
+| TPS | 33.6 tok/s | **45.9 tok/s** |
+| TTFT | 1486 ms | 1090 ms |
+### QuantZhai Ranking
+**Rank #15 of 47** — combined score 63.6 (equal-weight: TPS, PPL, convergence).
+Higher than many larger dense models — REAP pruning + IQ4_NL quantization is an efficient combination.
+## Usage
+```bash
+llama-cli -m Qwen3.5-24B-A3B-REAP-0.32-IQ4_NL.gguf \
+  -p "Write a mergesort in Python" \
+  -n 1024 -t 12 --temp 0.6 --top-p 0.95
+llama-server -m Qwen3.5-24B-A3B-REAP-0.32-IQ4_NL.gguf \
+  --host 0.0.0.0 --port 8080 -ngl 99 -t 12 \
+  --cache-type-k q8_0 --cache-type-v turbo3
+```
+## License
+Apache 2.0 (this quantization).
+Source model by [sandeshrajx](https://huggingface.co/sandeshrajx) under Apache 2.0 — see [arxiv:2510.13999](https://arxiv.org/abs/2510.13999).