Instructions to use badr7/rapidchat-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 badr7/rapidchat-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 badr7/rapidchat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf badr7/rapidchat-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 badr7/rapidchat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf badr7/rapidchat-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 badr7/rapidchat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf badr7/rapidchat-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 badr7/rapidchat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf badr7/rapidchat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/badr7/rapidchat-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use badr7/rapidchat-GGUF with Ollama:
```
ollama run hf.co/badr7/rapidchat-GGUF:Q4_K_M
```
Unsloth Desktop

How to use badr7/rapidchat-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf badr7/rapidchat-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": "badr7/rapidchat-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use badr7/rapidchat-GGUF with Docker Model Runner:
```
docker model run hf.co/badr7/rapidchat-GGUF:Q4_K_M
```

Lemonade

How to use badr7/rapidchat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull badr7/rapidchat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.rapidchat-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use badr7/rapidchat-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 badr7/rapidchat-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 badr7/rapidchat-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use badr7/rapidchat-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf badr7/rapidchat-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 "badr7/rapidchat-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"

rapidchat GGUF

Quantized builds of rapidchat, the 9B airline-support fine-tune. These builds were not separately benchmarked: the 86.5 tau2-bench airline pass^1 is for the full bf16 model, and 4-bit quantization usually costs a little accuracy. Training data: rapidchat-data.

file	size class	use
rapidchat-Q4_K_M.gguf	~5-6 GB	phones and laptops
rapidchat-Q8_0.gguf	~9-10 GB	desktops, near-lossless
rapidchat-Q3_K_M.gguf	4.6 GB	phones with less memory, recommended small build
rapidchat-Q3_K_S.gguf	4.3 GB	tighter phones
rapidchat-Q2_K.gguf	3.8 GB	smallest, noticeably lower quality

run it

llama-server -hf badr7/rapidchat-GGUF:Q4_K_M --jinja

Works in any GGUF app (LM Studio, Ollama, phone apps that load GGUF).

Smoke test: Q4_K_M loads in llama.cpp and answers a flight-change request coherently (about 6 tok/s on 12 CPU threads).

Downloads last month: 212

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for badr7/rapidchat-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Adapter

badr7/rapidchat

Quantized

(2)

this model