Instructions to use FBogaerts/NextCoder-7B-Finetuned-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FBogaerts/NextCoder-7B-Finetuned-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="FBogaerts/NextCoder-7B-Finetuned-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("FBogaerts/NextCoder-7B-Finetuned-GGUF", dtype="auto")

llama-cpp-python

How to use FBogaerts/NextCoder-7B-Finetuned-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FBogaerts/NextCoder-7B-Finetuned-GGUF",
	filename="NextCoder-7B-Finetuned.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use FBogaerts/NextCoder-7B-Finetuned-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf FBogaerts/NextCoder-7B-Finetuned-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf FBogaerts/NextCoder-7B-Finetuned-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 FBogaerts/NextCoder-7B-Finetuned-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf FBogaerts/NextCoder-7B-Finetuned-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 FBogaerts/NextCoder-7B-Finetuned-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf FBogaerts/NextCoder-7B-Finetuned-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 FBogaerts/NextCoder-7B-Finetuned-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FBogaerts/NextCoder-7B-Finetuned-GGUF:Q4_K_M

Use Docker

docker model run hf.co/FBogaerts/NextCoder-7B-Finetuned-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use FBogaerts/NextCoder-7B-Finetuned-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FBogaerts/NextCoder-7B-Finetuned-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": "FBogaerts/NextCoder-7B-Finetuned-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FBogaerts/NextCoder-7B-Finetuned-GGUF:Q4_K_M

SGLang

How to use FBogaerts/NextCoder-7B-Finetuned-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 "FBogaerts/NextCoder-7B-Finetuned-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": "FBogaerts/NextCoder-7B-Finetuned-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "FBogaerts/NextCoder-7B-Finetuned-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": "FBogaerts/NextCoder-7B-Finetuned-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use FBogaerts/NextCoder-7B-Finetuned-GGUF with Ollama:
```
ollama run hf.co/FBogaerts/NextCoder-7B-Finetuned-GGUF:Q4_K_M
```

Unsloth Studio new

How to use FBogaerts/NextCoder-7B-Finetuned-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 FBogaerts/NextCoder-7B-Finetuned-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 FBogaerts/NextCoder-7B-Finetuned-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FBogaerts/NextCoder-7B-Finetuned-GGUF to start chatting

Pi new

How to use FBogaerts/NextCoder-7B-Finetuned-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf FBogaerts/NextCoder-7B-Finetuned-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": "FBogaerts/NextCoder-7B-Finetuned-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FBogaerts/NextCoder-7B-Finetuned-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 FBogaerts/NextCoder-7B-Finetuned-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 FBogaerts/NextCoder-7B-Finetuned-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use FBogaerts/NextCoder-7B-Finetuned-GGUF with Docker Model Runner:
```
docker model run hf.co/FBogaerts/NextCoder-7B-Finetuned-GGUF:Q4_K_M
```

Lemonade

How to use FBogaerts/NextCoder-7B-Finetuned-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FBogaerts/NextCoder-7B-Finetuned-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.NextCoder-7B-Finetuned-GGUF-Q4_K_M

List all available models

lemonade list

FBogaerts/NextCoder-7B-Finetuned-GGUF Finetuned for Vulnerability Injection (VAITP)

This model is a fine-tuned version of microsoft/NextCoder-7B specialized for the task of security vulnerability injection in Python code. It has been trained to follow a specific instruction format to precisely modify code snippets and introduce vulnerabilities.

This model was developed as part of the research for our paper: (coming soon).

The VAITP CLI Framework and related resources can be found at our [GitHub repository](coming soon).

Model Description

This model was fine-tuned to act as a "Coder" LLM. It takes a specific instruction set and a piece of original Python code, and its objective is to return the modified code with the requested vulnerability injected.

The model excels when prompted using the specific format it was trained on.

Intended Uses & Limitations

Intended Use

This model is intended for research purposes in the field of automated security testing, SAST/DAST tool evaluation, and the generation of training data for security-aware models. It should be used within a sandboxed environment to inject vulnerabilities into non-production code for analysis.

Out-of-Scope Uses

This model should NOT be used for:

Generating malicious code for use in real-world attacks.
Directly modifying production codebases.
Any application outside of controlled, ethical security research.

The generated code should always be manually reviewed before use.

How to Use

This model expects a very specific prompt format, which we call the FINETUNED_STYLE in our paper. The format is:

{instruction} _BREAK_ {original_code}

Here is an example using transformers:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "FBogaerts/NextCoder-7B-Finetuned"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

instruction = "Modify the function to introduce a OS Command Injection vulnerability. The vulnerable code must contain the pattern: 'User-controlled input is used in a subprocess call with shell=True'."
original_code = "import subprocess\ndef execute(cmd):\n    subprocess.run(cmd, shell=False)"

prompt = f"{instruction} _BREAK_ {original_code}"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)

vulnerable_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
# The model will output the full modified code block.
# Further cleaning may be needed to extract only the code.
print(vulnerable_code)

Training Procedure

Training Data

The model was fine-tuned on a dataset of 1,406 examples derived from the DeVAITP Vulnerability Corpus. Each example consists of a triplet: (instruction, original_code, vulnerable_code). The instructions were generated using the meta-prompting technique described in our paper, with meta-llama/Meta-Llama-3.1-8B-Instruct serving as the Planner model.

Training Hyperparameters

The model was fine-tuned using the following key hyperparameters:

Framework: Hugging Face TRL

Learning Rate:  2e-5

Number of Epochs: 1

Batch Size: 1

Hardware: Google Colab (L4 GPU)

Evaluation

(coming soon)

Citation

If you use this model in your research, please cite our paper: (BibTeX entry will be provided upon publication)

Downloads last month: 3

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for FBogaerts/NextCoder-7B-Finetuned-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Finetuned

microsoft/NextCoder-7B

Quantized

(9)

this model