Instructions to use raih443/sast-llm-qwen25_coder-sva with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use raih443/sast-llm-qwen25_coder-sva with Transformers:

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

pipe = pipeline("text-generation", model="raih443/sast-llm-qwen25_coder-sva")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("raih443/sast-llm-qwen25_coder-sva")
model = AutoModelForCausalLM.from_pretrained("raih443/sast-llm-qwen25_coder-sva", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use raih443/sast-llm-qwen25_coder-sva 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 raih443/sast-llm-qwen25_coder-sva
# Run inference directly in the terminal:
llama cli -hf raih443/sast-llm-qwen25_coder-sva

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf raih443/sast-llm-qwen25_coder-sva
# Run inference directly in the terminal:
llama cli -hf raih443/sast-llm-qwen25_coder-sva

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 raih443/sast-llm-qwen25_coder-sva
# Run inference directly in the terminal:
./llama-cli -hf raih443/sast-llm-qwen25_coder-sva

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 raih443/sast-llm-qwen25_coder-sva
# Run inference directly in the terminal:
./build/bin/llama-cli -hf raih443/sast-llm-qwen25_coder-sva

Use Docker

docker model run hf.co/raih443/sast-llm-qwen25_coder-sva

LM Studio
Jan

vLLM

How to use raih443/sast-llm-qwen25_coder-sva with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "raih443/sast-llm-qwen25_coder-sva"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raih443/sast-llm-qwen25_coder-sva",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/raih443/sast-llm-qwen25_coder-sva

SGLang

How to use raih443/sast-llm-qwen25_coder-sva 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 "raih443/sast-llm-qwen25_coder-sva" \
    --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": "raih443/sast-llm-qwen25_coder-sva",
		"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 "raih443/sast-llm-qwen25_coder-sva" \
        --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": "raih443/sast-llm-qwen25_coder-sva",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use raih443/sast-llm-qwen25_coder-sva with Ollama:
```
ollama run hf.co/raih443/sast-llm-qwen25_coder-sva
```

Unsloth Studio

How to use raih443/sast-llm-qwen25_coder-sva 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 raih443/sast-llm-qwen25_coder-sva 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 raih443/sast-llm-qwen25_coder-sva to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for raih443/sast-llm-qwen25_coder-sva to start chatting

How to use raih443/sast-llm-qwen25_coder-sva with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf raih443/sast-llm-qwen25_coder-sva

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": "raih443/sast-llm-qwen25_coder-sva"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use raih443/sast-llm-qwen25_coder-sva with Docker Model Runner:
```
docker model run hf.co/raih443/sast-llm-qwen25_coder-sva
```

Lemonade

How to use raih443/sast-llm-qwen25_coder-sva with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull raih443/sast-llm-qwen25_coder-sva

Run and chat with the model

lemonade run user.sast-llm-qwen25_coder-sva-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use raih443/sast-llm-qwen25_coder-sva with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf raih443/sast-llm-qwen25_coder-sva

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 raih443/sast-llm-qwen25_coder-sva

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use raih443/sast-llm-qwen25_coder-sva with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf raih443/sast-llm-qwen25_coder-sva

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 "raih443/sast-llm-qwen25_coder-sva" \
  --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"

SAST-LLM — QWEN25_CODER fine-tuned for SVA

Static Analysis Security Tool berbasis LLM. Fine-tuned dari Qwen/Qwen2.5-Coder-7B-Instruct menggunakan QLoRA untuk mendeteksi kerentanan keamanan pada kode C/C++.

Model Details

Parameter	Value
Base Model	`Qwen/Qwen2.5-Coder-7B-Instruct`
Training Type	`SVA`
Identifier	`PERCOBAAN_2`
LoRA Rank	`8`
LoRA Alpha	`32`
Max Seq Length	`512`
Epochs	`3`
Effective Batch	`64`
Learning Rate	`0.0002`
Completed	`2026-05-16T19:22:11.717011`

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "raih443/sast-llm-qwen25_coder-sva"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype = torch.float16,
    device_map  = "auto",
)

# Contoh analisis vulnerability
code = (
    "int main() {\n"
    "    char buffer[10];\n"
    "    gets(buffer);  // buffer overflow!\n"
    "    return 0;\n"
"}")

messages = [
    {"role": "system",
     "content": "You are a security vulnerability detector. "
                "Respond with 1 if vulnerable or 0 if safe."},
    {"role": "user",
     "content": f"Analyze this C code:\n```c\n{code}\n```"},
]

text   = tokenizer.apply_chat_template(messages, tokenize=False,
                                        add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens = 50,
        temperature    = 0.1,
        do_sample      = False,
    )

response = tokenizer.decode(
    outputs[0][inputs["input_ids"].shape[1]:],
    skip_special_tokens=True
)
print(response)  # "1" = vulnerable, "0" = safe

Training Details

Dataset: C/C++ code snippets dengan label kerentanan (CWE-based)
Method: QLoRA (4-bit NF4 quantization + LoRA adapter)
Task: Binary vulnerability classification (1 = vulnerable, 0 = safe)
Training Type: SVA

GGUF

Model tersedia dalam format GGUF (f16) di folder gguf/ untuk dipakai dengan llama.cpp / Ollama.

Limitations

Dilatih pada C/C++ code — performa pada bahasa lain tidak dijamin
Output biner (1/0) tanpa penjelasan detail
False positive/negative mungkin terjadi pada kode kompleks

Citation

@misc{sast-llm-percobaan_2,
  title  = {SAST-LLM: Fine-tuned QWEN25_CODER for Vulnerability Detection},
  author = {raih443},
  year   = {2026},
}

Downloads last month: 18

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for raih443/sast-llm-qwen25_coder-sva

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

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

Adapter

(741)

this model