Instructions to use Outlier-Ai/Outlier-Max-32B-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 Outlier-Ai/Outlier-Max-32B-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 Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M

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 Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M

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 Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Outlier-Ai/Outlier-Max-32B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Outlier-Ai/Outlier-Max-32B-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": "Outlier-Ai/Outlier-Max-32B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M

Ollama
How to use Outlier-Ai/Outlier-Max-32B-GGUF with Ollama:
```
ollama run hf.co/Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Outlier-Ai/Outlier-Max-32B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Outlier-Ai/Outlier-Max-32B-GGUF with Docker Model Runner:
```
docker model run hf.co/Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M
```

Lemonade

How to use Outlier-Ai/Outlier-Max-32B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Outlier-Max-32B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Outlier-Ai/Outlier-Max-32B-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 Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M

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 Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Outlier-Ai/Outlier-Max-32B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M

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 "Outlier-Ai/Outlier-Max-32B-GGUF:Q4_K_M" \
  --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"

ur-dad-matt commited on May 4

Commit

be9b1c7

verified ·

1 Parent(s): e513abc

docs: pre-v1.8 banner + funnel to MLX lineup

Browse files

Files changed (1) hide show

README.md +62 -46

README.md CHANGED Viewed

@@ -6,64 +6,80 @@ base_model: Qwen/Qwen2.5-32B-Instruct
 base_model_relation: quantized
 quantized_by: Outlier-Ai
 tags:
-  - gguf
-  - llama-cpp
-  - llama.cpp
-  - ollama
-  - lm-studio
-  - jan
-  - quantized
-  - 4bit
-  - 4-bit
-  - 5-bit
-  - 8-bit
-  - local-llm
-  - on-device
-  - cpu
-  - edge-ai
-  - offline
-  - outlier
-  - outlier-app
-  - qwen2.5
-  - qwen
-  - text-generation
-  - conversational
-  - chat
-  - instruct
-  - windows
-  - linux
-  - mac
-  - function-calling
 language:
-  - en
-  - zh
-  - fr
-  - es
-  - pt
-  - de
-  - it
-  - ru
-  - ja
-  - ko
-  - ar
-  - vi
-  - th
-  - nl
-  - pl
 widget:
 - example_title: General knowledge
   messages:
   - role: user
-    content: "Explain how mixture-of-experts models work in plain language, for a curious but non-technical reader."
 - example_title: Privacy rationale
   messages:
   - role: user
-    content: "What are three practical reasons someone might want to run an LLM locally instead of in the cloud?"
 - example_title: Summarize
   messages:
   - role: user
-    content: "Summarize the differences between 4-bit and 8-bit quantization in two short paragraphs."
 ---
 # Outlier Max 32B (GGUF)

 base_model_relation: quantized
 quantized_by: Outlier-Ai
 tags:
+- gguf
+- llama-cpp
+- llama.cpp
+- ollama
+- lm-studio
+- jan
+- quantized
+- 4bit
+- 4-bit
+- 5-bit
+- 8-bit
+- local-llm
+- on-device
+- cpu
+- edge-ai
+- offline
+- outlier
+- outlier-app
+- qwen2.5
+- qwen
+- text-generation
+- conversational
+- chat
+- instruct
+- windows
+- linux
+- mac
+- function-calling
 language:
+- en
+- zh
+- fr
+- es
+- pt
+- de
+- it
+- ru
+- ja
+- ko
+- ar
+- vi
+- th
+- nl
+- pl
 widget:
 - example_title: General knowledge
   messages:
   - role: user
+    content: Explain how mixture-of-experts models work in plain language, for a curious
+      but non-technical reader.
 - example_title: Privacy rationale
   messages:
   - role: user
+    content: What are three practical reasons someone might want to run an LLM locally
+      instead of in the cloud?
 - example_title: Summarize
   messages:
   - role: user
+    content: Summarize the differences between 4-bit and 8-bit quantization in two
+      short paragraphs.
 ---
+> # ⚠️ Pre-v1.8 build — see [Outlier-Ai org](https://huggingface.co/Outlier-Ai) for current MLX 4-bit lineup
+>
+> This is a **Max (deferred to v1.9+ — see Plus tier on Outlier-Ai org) GGUF** build from a pre-v1.8 era. The current Outlier app (v1.8+) ships **MLX 4-bit** versions optimized for Apple Silicon — much faster than GGUF on Mac.
+>
+> | Want | Use |
+> |---|---|
+> | **Run on macOS** (any M-series Mac) | [Outlier-Ai (MLX 4-bit lineup)](https://huggingface.co/Outlier-Ai) |
+> | **Run on Windows / Linux** | This GGUF still works with [llama.cpp](https://github.com/ggerganov/llama.cpp), [Ollama](https://ollama.ai), [LM Studio](https://lmstudio.ai), [Jan](https://jan.ai) |
+>
+> [📥 Download the Outlier desktop app](https://outlier.host)
+---
 # Outlier Max 32B (GGUF)