Instructions to use nassimjp/ipashtoCoder-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nassimjp/ipashtoCoder-9B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="nassimjp/ipashtoCoder-9B-GGUF",
	filename="omnicoder-9b-bf16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use nassimjp/ipashtoCoder-9B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nassimjp/ipashtoCoder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nassimjp/ipashtoCoder-9B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf nassimjp/ipashtoCoder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf nassimjp/ipashtoCoder-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 nassimjp/ipashtoCoder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nassimjp/ipashtoCoder-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 nassimjp/ipashtoCoder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nassimjp/ipashtoCoder-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/nassimjp/ipashtoCoder-9B-GGUF:Q4_K_M

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

Unsloth Studio

How to use nassimjp/ipashtoCoder-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 nassimjp/ipashtoCoder-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 nassimjp/ipashtoCoder-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 nassimjp/ipashtoCoder-9B-GGUF to start chatting

How to use nassimjp/ipashtoCoder-9B-GGUF with Pi:

Start the llama.cpp server

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

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nassimjp/ipashtoCoder-9B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf nassimjp/ipashtoCoder-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 nassimjp/ipashtoCoder-9B-GGUF:Q4_K_M

Run Hermes

hermes

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

Lemonade

How to use nassimjp/ipashtoCoder-9B-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

ipashtoCoder-9B-GGUF

File size: 1,896 Bytes

00a7863

---
base_model: Tesslate/OmniCoder-9B
tags:
  - llama-cpp
  - gguf
  - qwen3.5
  - omnicoder
  - tesslate
  - code
  - agent
license: apache-2.0
---

<div align="center">

<img src="https://huggingface.co/Tesslate/OmniCoder-9B/resolve/main/omnicoder-banner.png" alt="OmniCoder" width="720">

# OmniCoder-9B-GGUF

### GGUF quantizations of [OmniCoder-9B](https://huggingface.co/Tesslate/OmniCoder-9B)

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Full Weights](https://img.shields.io/badge/Full_Weights-OmniCoder--9B-purple)](https://huggingface.co/Tesslate/OmniCoder-9B)

</div>

---

## 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

```bash
# 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
```

---

<div align="center">

**Built by [Tesslate](https://tesslate.com)** | See full model card: [OmniCoder-9B](https://huggingface.co/Tesslate/OmniCoder-9B)

</div>