Instructions to use QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF",
	filename="Llama-3-Patronus-Lynx-8B-Instruct.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/Llama-3-Patronus-Lynx-8B-Instruct-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/Llama-3-Patronus-Lynx-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-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/Llama-3-Patronus-Lynx-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-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/Llama-3-Patronus-Lynx-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-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/Llama-3-Patronus-Lynx-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-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/Llama-3-Patronus-Lynx-8B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-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/Llama-3-Patronus-Lynx-8B-Instruct-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/Llama-3-Patronus-Lynx-8B-Instruct-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/Llama-3-Patronus-Lynx-8B-Instruct-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/Llama-3-Patronus-Lynx-8B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-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/Llama-3-Patronus-Lynx-8B-Instruct-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/Llama-3-Patronus-Lynx-8B-Instruct-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/Llama-3-Patronus-Lynx-8B-Instruct-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3-Patronus-Lynx-8B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF

This is quantized version of PatronusAI/Llama-3-Patronus-Lynx-8B-Instruct created using llama.cpp

Model Description

Lynx is an open-source hallucination evaluation model. Patronus-Lynx-8B-Instruct was trained on a mix of datasets including CovidQA, PubmedQA, DROP, RAGTruth. The datasets contain a mix of hand-annotated and synthetic data. The maximum sequence length is 8000 tokens.

Model Details

Model Type: Patronus-Lynx-8B-Instruct is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct model.
Language: Primarily English
Developed by: Patronus AI
License: https://creativecommons.org/licenses/by-nc/4.0/

Model Sources

Repository: https://github.com/patronus-ai/Lynx-hallucination-detection

How to Get Started with the Model

The model is fine-tuned to be used to detect hallucinations in a RAG setting. Provided a document, question and answer, the model can evaluate whether the answer is faithful to the document.

To use the model, we recommend using the prompt we used for fine-tuning:

PROMPT = """
Given the following QUESTION, DOCUMENT and ANSWER you must analyze the provided answer and determine whether it is faithful to the contents of the DOCUMENT. The ANSWER must not offer new information beyond the context provided in the DOCUMENT. The ANSWER also must not contradict information provided in the DOCUMENT. Output your final verdict by strictly following this format: "PASS" if the answer is faithful to the DOCUMENT and "FAIL" if the answer is not faithful to the DOCUMENT. Show your reasoning.

--
QUESTION (THIS DOES NOT COUNT AS BACKGROUND INFORMATION):
{question}

--
DOCUMENT:
{context}

--
ANSWER:
{answer}

--

Your output should be in JSON FORMAT with the keys "REASONING" and "SCORE":
{{"REASONING": <your reasoning as bullet points>, "SCORE": <your final score>}}
"""

The model will output the score as 'PASS' if the answer is faithful to the document or FAIL if the answer is not faithful to the document.

Training Details

The model was finetuned for 3 epochs using H100s on dataset of size 2400. We use lion optimizer with lr=5.0e-7. For more details on data generation, please check out our Github repo.

Training Data

We train on 2400 samples consisting of CovidQA, PubmedQA, DROP and RAGTruth samples. For datasets that do not contain hallucinated samples, we generate perturbations to introduce hallucinations in the data. For more details about the data generation process, refer to the paper.

Evaluation

The model was evaluated on PatronusAI/HaluBench.

It outperforms GPT-3.5-Turbo, GPT-4-Turbo, GPT-4o and Claude Sonnet.

Model Card Contact

@sunitha-ravi

Downloads last month: 438

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Llama-3-Patronus-Lynx-8B-Instruct-GGUF

Base model

PatronusAI/Llama-3-Patronus-Lynx-8B-Instruct

Quantized

(7)

this model