Instructions to use Tesslate/OmniCoder-9B-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 Tesslate/OmniCoder-9B-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 Tesslate/OmniCoder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Tesslate/OmniCoder-9B-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 Tesslate/OmniCoder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Tesslate/OmniCoder-9B-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 Tesslate/OmniCoder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Tesslate/OmniCoder-9B-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 Tesslate/OmniCoder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Tesslate/OmniCoder-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Tesslate/OmniCoder-9B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Tesslate/OmniCoder-9B-GGUF with Ollama:
```
ollama run hf.co/Tesslate/OmniCoder-9B-GGUF:Q4_K_M
```

Unsloth Studio

How to use Tesslate/OmniCoder-9B-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 Tesslate/OmniCoder-9B-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 Tesslate/OmniCoder-9B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Tesslate/OmniCoder-9B-GGUF to start chatting

How to use Tesslate/OmniCoder-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Tesslate/OmniCoder-9B-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": "Tesslate/OmniCoder-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Tesslate/OmniCoder-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/Tesslate/OmniCoder-9B-GGUF:Q4_K_M
```

Lemonade

How to use Tesslate/OmniCoder-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Tesslate/OmniCoder-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OmniCoder-9B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Tesslate/OmniCoder-9B-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 Tesslate/OmniCoder-9B-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 Tesslate/OmniCoder-9B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Tesslate/OmniCoder-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Tesslate/OmniCoder-9B-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 "Tesslate/OmniCoder-9B-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"

OmniCoder-9B-GGUF

GGUF quantizations of OmniCoder-9B

Available Quantizations

Quantization	Size	Use Case
`Q2_K`	~3.8 GB	Extreme compression, lowest quality
`Q3_K_S`	~4.3 GB	Small footprint
`Q3_K_M`	~4.6 GB	Small footprint, balanced
`Q3_K_L`	~4.9 GB	Small footprint, higher quality
`Q4_0`	~5.3 GB	Good balance
`Q4_K_S`	~5.4 GB	Good balance
`Q4_K_M`	~5.7 GB	Recommended for most users
`Q5_0`	~6.3 GB	High quality
`Q5_K_S`	~6.3 GB	High quality
`Q5_K_M`	~6.5 GB	High quality, balanced
`Q6_K`	~7.4 GB	Near-lossless
`Q8_0`	~9.5 GB	Highest quality quantization
`BF16`	~17.9 GB	Full precision

Usage

# Install llama.cpp
brew install llama.cpp  # macOS
# or build from source: https://github.com/ggml-org/llama.cpp

# Interactive chat
llama-cli --hf-repo Tesslate/OmniCoder-9B-GGUF --hf-file omnicoder-9b-q4_k_m.gguf -p "Your prompt" -c 8192

# Server mode (OpenAI-compatible API)
llama-server --hf-repo Tesslate/OmniCoder-9B-GGUF --hf-file omnicoder-9b-q4_k_m.gguf -c 8192

Built by Tesslate | See full model card: OmniCoder-9B

Downloads last month: 7,708

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

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

Model tree for Tesslate/OmniCoder-9B-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

Tesslate/OmniCoder-9B

Quantized

(21)

this model

Spaces using Tesslate/OmniCoder-9B-GGUF 3

Collection including Tesslate/OmniCoder-9B-GGUF

OmniCoder

Collection

2 items • Updated Mar 12 • 6