Instructions to use concedo/Beepo-22B-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 concedo/Beepo-22B-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 concedo/Beepo-22B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf concedo/Beepo-22B-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 concedo/Beepo-22B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf concedo/Beepo-22B-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 concedo/Beepo-22B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf concedo/Beepo-22B-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 concedo/Beepo-22B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf concedo/Beepo-22B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/concedo/Beepo-22B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use concedo/Beepo-22B-GGUF with Ollama:
```
ollama run hf.co/concedo/Beepo-22B-GGUF:Q4_K_M
```

Unsloth Studio

How to use concedo/Beepo-22B-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 concedo/Beepo-22B-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 concedo/Beepo-22B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for concedo/Beepo-22B-GGUF to start chatting

How to use concedo/Beepo-22B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf concedo/Beepo-22B-GGUF:Q4_K_M

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": "concedo/Beepo-22B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use concedo/Beepo-22B-GGUF with Docker Model Runner:
```
docker model run hf.co/concedo/Beepo-22B-GGUF:Q4_K_M
```

Lemonade

How to use concedo/Beepo-22B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull concedo/Beepo-22B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Beepo-22B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use concedo/Beepo-22B-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 concedo/Beepo-22B-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 concedo/Beepo-22B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use concedo/Beepo-22B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf concedo/Beepo-22B-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 "concedo/Beepo-22B-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"

Beepo-22B-GGUF

This is the GGUF quantization of https://huggingface.co/concedo/Beepo-22B, which was originally finetuned on top of the https://huggingface.co/mistralai/Mistral-Small-Instruct-2409 model.

You can use KoboldCpp to run this model.

Key Features:

Retains Intelligence - LR was kept low and dataset heavily pruned to avoid losing too much of the original model's intelligence.
Instruct prompt format supports Alpaca - Honestly, I don't know why more models don't use it. If you are an Alpaca format lover like me, this should help. The original Mistral instruct format can still be used, but is not recommended.
Instruct Decensoring Applied - You should not need a jailbreak for a model to obey the user. The model should always do what you tell it to. No need for weird "Sure, I will" or kitten-murdering-threat tricks. No abliteration was done, only finetuning. This model is not evil. It does not judge or moralize. Like a good tool, it simply obeys.

Prompt template: Alpaca

### Instruction:
{prompt}

### Response:

Please leave any feedback or issues that you may have.

Downloads last month: 827

GGUF

Model size

22B params

Architecture

llama

Hardware compatibility

2-bit

4-bit

6-bit

8-bit

16-bit

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