Instructions to use QuantFactory/Starling-LM-7B-beta-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Starling-LM-7B-beta-GGUF with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Starling-LM-7B-beta-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Starling-LM-7B-beta-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Starling-LM-7B-beta-GGUF",
	filename="Starling-LM-7B-beta.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/Starling-LM-7B-beta-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/Starling-LM-7B-beta-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Starling-LM-7B-beta-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/Starling-LM-7B-beta-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Starling-LM-7B-beta-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/Starling-LM-7B-beta-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Starling-LM-7B-beta-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/Starling-LM-7B-beta-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Starling-LM-7B-beta-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Starling-LM-7B-beta-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Starling-LM-7B-beta-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/QuantFactory/Starling-LM-7B-beta-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Starling-LM-7B-beta-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/Starling-LM-7B-beta-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/Starling-LM-7B-beta-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/Starling-LM-7B-beta-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/Starling-LM-7B-beta-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Starling-LM-7B-beta-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Starling-LM-7B-beta-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Starling-LM-7B-beta-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/Starling-LM-7B-beta-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/Starling-LM-7B-beta-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/Starling-LM-7B-beta-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Starling-LM-7B-beta-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Starling-LM-7B-beta-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Starling-LM-7B-beta-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Starling-LM-7B-beta-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Starling-LM-7B-beta-GGUF-Q4_K_M

List all available models

lemonade list

Starling-LM-7B-beta-GGUF

Model creator: Nexusflow
Original model: Starling-LM-7B-beta

Description

This repo contains GGUF format model files for Starling-LM-7B-beta

Model Summary

Developed by: The Nexusflow Team ( Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu, Karthik Ganesan, Wei-Lin Chiang, Jian Zhang, and Jiantao Jiao).
Model type: Language Model finetuned with RLHF / RLAIF
License: Apache-2.0 license under the condition that the model is not used to compete with OpenAI
Finetuned from model: Openchat-3.5-0106 (based on Mistral-7B-v0.1)

We introduce Starling-LM-7B-beta, an open large language model (LLM) trained by Reinforcement Learning from AI Feedback (RLAIF). Starling-LM-7B-beta is trained from Openchat-3.5-0106 with our new reward model Nexusflow/Starling-RM-34B and policy optimization method Fine-Tuning Language Models from Human Preferences (PPO). Harnessing the power of the ranking dataset, berkeley-nest/Nectar, the upgraded reward model, Starling-RM-34B, and the new reward training and policy tuning pipeline, Starling-LM-7B-beta scores an improved 8.12 in MT Bench with GPT-4 as a judge.

Citation

@misc{starling2023,
    title = {Starling-7B: Improving LLM Helpfulness & Harmlessness with RLAIF},
    url = {},
    author = {Zhu, Banghua and Frick, Evan and Wu, Tianhao and Zhu, Hanlin and Ganesan, Karthik and Chiang, Wei-Lin and Zhang, Jian and Jiao, Jiantao},
    month = {November},
    year = {2023}
}

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GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

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Base model

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Paper for QuantFactory/Starling-LM-7B-beta-GGUF

Fine-Tuning Language Models from Human Preferences

Paper • 1909.08593 • Published Sep 18, 2019 • 4