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

Libraries

How to use prithivMLmods/OxCoder-9B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="prithivMLmods/OxCoder-9B-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/OxCoder-9B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/OxCoder-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 prithivMLmods/OxCoder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/OxCoder-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 prithivMLmods/OxCoder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/OxCoder-9B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf prithivMLmods/OxCoder-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf prithivMLmods/OxCoder-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/OxCoder-9B-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use prithivMLmods/OxCoder-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/OxCoder-9B-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": "prithivMLmods/OxCoder-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/OxCoder-9B-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/OxCoder-9B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/OxCoder-9B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/OxCoder-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/OxCoder-9B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/OxCoder-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/OxCoder-9B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/OxCoder-9B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use prithivMLmods/OxCoder-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 prithivMLmods/OxCoder-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": "prithivMLmods/OxCoder-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use prithivMLmods/OxCoder-9B-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/OxCoder-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 prithivMLmods/OxCoder-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 prithivMLmods/OxCoder-9B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/OxCoder-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 prithivMLmods/OxCoder-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 "prithivMLmods/OxCoder-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"

OxCoder-9B-GGUF

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---
base_model:
- OrionLLM/OxCoder-9B
license: apache-2.0
library_name: transformers
tags:
- text-generation-inference
- llama-cpp
- qwen3_5
- coder
- 9b
- agentic
- coding
language:
- en
pipeline_tag: image-text-to-text
---

# **OxCoder-9B-GGUF**

> [OxCoder-9B](https://huggingface.co/OrionLLM/OxCoder-9B) is developed by OrionLLM, a lightweight 9-billion-parameter coding model built on Qwen3.5-9B for long-horizon agentic coding, terminal automation, and agentic reasoning, distilled from frontier agentic trajectories generated by Fable-5.1 and GLM-5.3 across Claude Code, OpenCode, and Codex environments. It ships with a full 262,144-token native context window for handling complex multi-file codebases, learns error-recovery behaviors like read-before-write patterns and LSP-diagnostic-driven minimal edit diffs rather than full rewrites, and exhibits notably strong front-end reasoning for a sub-10B model, with deep understanding of UI logic and component architecture. Despite its compact size, OxCoder-9B punches well above its weight class, outperforming Ornith-1.5-9B, Ornith-1.0-9B, Qwen3.5-9B, and even the larger Gemma-4-31B across nearly every reported benchmark — including 50.8 on Terminal-Bench 2.1 (Claude Code), 73.5 on SWE-bench Verified, 49.1 on SWE-bench Pro, 86.9 on GPQA Diamond, and 67.8 on ClawEval — reflecting particular strength in agentic terminal coding and real-world software engineering tasks relative to its parameter count. The model is released under the Apache 2.0 License.

## Model Files

   File Name | Quant Type | File Size | File Link |
 |-----------|------------|-----------|-----------|
 | OxCoder-9B.BF16.gguf | BF16 | 17.9 GB | [Download](https://huggingface.co/prithivMLmods/OxCoder-9B-GGUF/blob/main/OxCoder-9B.BF16.gguf) |
 | OxCoder-9B.Q3_K_L.gguf | Q3_K_L | 4.93 GB | [Download](https://huggingface.co/prithivMLmods/OxCoder-9B-GGUF/blob/main/OxCoder-9B.Q3_K_L.gguf) |
 | OxCoder-9B.Q3_K_M.gguf | Q3_K_M | 4.62 GB | [Download](https://huggingface.co/prithivMLmods/OxCoder-9B-GGUF/blob/main/OxCoder-9B.Q3_K_M.gguf) |
 | OxCoder-9B.Q3_K_S.gguf | Q3_K_S | 4.26 GB | [Download](https://huggingface.co/prithivMLmods/OxCoder-9B-GGUF/blob/main/OxCoder-9B.Q3_K_S.gguf) |
 | OxCoder-9B.Q4_0.gguf | Q4_0 | 5.31 GB | [Download](https://huggingface.co/prithivMLmods/OxCoder-9B-GGUF/blob/main/OxCoder-9B.Q4_0.gguf) |
 | OxCoder-9B.Q4_K_M.gguf | Q4_K_M | 5.63 GB | [Download](https://huggingface.co/prithivMLmods/OxCoder-9B-GGUF/blob/main/OxCoder-9B.Q4_K_M.gguf) |
 | OxCoder-9B.Q4_K_S.gguf | Q4_K_S | 5.35 GB | [Download](https://huggingface.co/prithivMLmods/OxCoder-9B-GGUF/blob/main/OxCoder-9B.Q4_K_S.gguf) |
 | OxCoder-9B.Q5_0.gguf | Q5_0 | 6.31 GB | [Download](https://huggingface.co/prithivMLmods/OxCoder-9B-GGUF/blob/main/OxCoder-9B.Q5_0.gguf) |
 | OxCoder-9B.Q5_K_M.gguf | Q5_K_M | 6.47 GB | [Download](https://huggingface.co/prithivMLmods/OxCoder-9B-GGUF/blob/main/OxCoder-9B.Q5_K_M.gguf) |
 | OxCoder-9B.Q5_K_S.gguf | Q5_K_S | 6.31 GB | [Download](https://huggingface.co/prithivMLmods/OxCoder-9B-GGUF/blob/main/OxCoder-9B.Q5_K_S.gguf) |
 | OxCoder-9B.mmproj-bf16.gguf | mmproj-bf16 | 922 MB | [Download](https://huggingface.co/prithivMLmods/OxCoder-9B-GGUF/blob/main/OxCoder-9B.mmproj-bf16.gguf) |

## llama.cpp

LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp