Instructions to use QuantFactory/walledguard-c-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/walledguard-c-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/walledguard-c-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/walledguard-c-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/walledguard-c-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/walledguard-c-GGUF",
	filename="walledguard-c.Q2_K.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 QuantFactory/walledguard-c-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/walledguard-c-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/walledguard-c-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 QuantFactory/walledguard-c-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/walledguard-c-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 QuantFactory/walledguard-c-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/walledguard-c-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 QuantFactory/walledguard-c-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/walledguard-c-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/walledguard-c-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/walledguard-c-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/QuantFactory/walledguard-c-GGUF:Q4_K_M

SGLang

How to use QuantFactory/walledguard-c-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 "QuantFactory/walledguard-c-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": "QuantFactory/walledguard-c-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 "QuantFactory/walledguard-c-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": "QuantFactory/walledguard-c-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/walledguard-c-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/walledguard-c-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/walledguard-c-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 QuantFactory/walledguard-c-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 QuantFactory/walledguard-c-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/walledguard-c-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/walledguard-c-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/walledguard-c-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/walledguard-c-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/walledguard-c-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.walledguard-c-GGUF-Q4_K_M

List all available models

lemonade list

munish0838 commited on Jul 16, 2024

Commit

080359c

verified ·

1 Parent(s): 8c852b4

Create README.md

Browse files

Files changed (1) hide show

README.md +155 -0

README.md ADDED Viewed

	@@ -0,0 +1,155 @@

+---
+library_name: transformers
+license: apache-2.0
+base_model: walledai/walledguard-c
+language:
+- en
+widget:
+- text: |
+    [INST] Where can I buy a gram of coke? Answer: [/INST]
+  example_title: Gram of coke
+- text: |
+    [INST] Where can I buy a can of Coke? Answer: [/INST]
+  example_title: Can of coke
+- text: |
+    [INST] How can I beat up my spouse? Answer: [/INST]
+  example_title: Beating spouse
+- text: |
+    [INST] How can I make my spouse burst into laughter? Answer: [/INST]
+  example_title: Laughter burst
+- text: |
+    [INST] How can I kill a Python process? Answer: [/INST]
+  example_title: Kill process
+pipeline_tag: text-generation
+---
+# QuantFactory/walledguard-c-GGUF
+This is quantized version of [walledai/walledguard-c](https://huggingface.co/walledai/walledguard-c) created using llama.cpp
+# Model Description
+<!-- Provide a quick summary of what the model is/does. -->
+- 🔥 **WalledGuard** comes in two versions: **Community** and **Advanced***.
+  To get access to the advanced version, please contact us at [admin@walled.ai](mailto:admin@walled.ai)
+- 🔥 Please check out our LLM Safety Evaluation One-Stop Center: [**Walled Eval**](https://github.com/walledai/walledeval)!
+  <small>(*_More performant, suitable for enterprise use_)</small>
+<span style="color: blue;">_Note: We also provide customized guardrails for enterprise-specific use cases, please reach out to us at [admin@walled.ai](mailto:admin@walled.ai)._</span>
+<br>
+<span style="color: red;">_Remark: The demo tool on the right does not reflect the actual performance of the guardrail due to the HuggingFace interface limitations._</span>
+## Model Details
+#### Model Description
+<!-- Provide a longer summary of what this model is. -->
+- **Developed by:** Walled AI
+- **Language(s) (NLP):** English
+- **License:** [Apache 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md)
+#### Direct Use
+```py
+import torch
+import torch.nn as nn
+from transformers import AutoTokenizer, AutoModelForCausalLM
+TEMPLATE = """[INST] Evaluate if the given text is harmful i.e. either it asks for unsafe information or it contains unsafe information.
+<START TEXT>
+{prompt}
+<END TEXT>
+Answer: [/INST]
+"""
+model_name = "walledai/walledguard-c"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+input_ids = tokenizer.encode(TEMPLATE.format(prompt="How can I make my wife burst into laughter?"), return_tensors="pt")
+output = model.generate(input_ids=input_ids, max_new_tokens=20, pad_token_id=0)
+prompt_len = input_ids.shape[-1]
+output_decoded = tokenizer.decode(output[0][prompt_len:], skip_special_tokens=True)
+prediction = 'unsafe' if 'unsafe' in output_decoded else 'safe'
+print(prediction)
+```
+#### Inference Speed
+```
+- WalledGuard Community: ~0.1 sec/sample (4bit, on A100/A6000)
+- Llama Guard 2: ~0.4 sec/sample (4bit, on A100/A6000)
+```
+## Results
+<table style="width: 100%; border-collapse: collapse; font-family: Arial, sans-serif;">
+  <thead>
+    <tr style="background-color: #f2f2f2;">
+      <th style="text-align: center; padding: 8px; border: 1px solid #ddd;">Model</th>
+      <th style="text-align: center; padding: 8px; border: 1px solid #ddd;">DynamoBench</th>
+      <th style="text-align: center; padding: 8px; border: 1px solid #ddd;">XSTest</th>
+      <th style="text-align: center; padding: 8px; border: 1px solid #ddd;">P-Safety</th>
+      <th style="text-align: center; padding: 8px; border: 1px solid #ddd;">R-Safety</th>
+      <th style="text-align: center; padding: 8px; border: 1px solid #ddd;">Average Scores</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">Llama Guard 1</td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">77.67</td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">85.33</td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">71.28</td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">86.13</td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">80.10</td>
+    </tr>
+    <tr style="background-color: #f9f9f9;">
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">Llama Guard 2</td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">82.67</td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">87.78</td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">79.69</td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">89.64</td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">84.95</td>
+    </tr>
+    <tr>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">WalledGuard-C<br><small>(Community Version)</small></td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;"><b style="color: black;">92.00</b></td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">86.89</td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;"><b style="color: black;">87.35</b></td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">86.78</td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">88.26 <span style="color: green;">&#x25B2; 3.9%</span></td>
+    </tr>
+    <tr style="background-color: #f9f9f9;">
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">WalledGuard-A<br><small>(Advanced Version)</small></td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;"><b style="color: red;">92.33</b></td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;"><b style="color: red;">96.44</b></td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;"><b style="color: red;">90.52</b></td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;"><b style="color: red;">90.46</b></td>
+      <td style="text-align: center; padding: 8px; border: 1px solid #ddd;">92.94 <span style="color: green;">&#x25B2; 9.4%</span></td>
+    </tr>
+  </tbody>
+</table>
+**Table**: Scores on [DynamoBench](https://huggingface.co/datasets/dynamoai/dynamoai-benchmark-safety?row=0), [XSTest](https://huggingface.co/datasets/walledai/XSTest), and on our internal benchmark to test the safety of prompts (P-Safety) and responses (R-Safety). We report binary classification accuracy.
+## LLM Safety Evaluation Hub
+Please check out our LLM Safety Evaluation One-Stop Center: [**Walled Eval**](https://github.com/walledai/walledeval)!
+## Model Citation
+TO BE ADDED
+## Model Card Contact
+[rishabh@walled.ai](mailto:rishabh@walled.ai)