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

Libraries

How to use QuantFactory/zephyr-7b-beta-GGUF with Transformers:

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

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

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

llama-cpp-python

How to use QuantFactory/zephyr-7b-beta-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

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

Use Docker

docker model run hf.co/QuantFactory/zephyr-7b-beta-GGUF:Q4_K_M

LM Studio
Jan

vLLM

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

Use Docker

docker model run hf.co/QuantFactory/zephyr-7b-beta-GGUF:Q4_K_M

SGLang

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

Ollama
How to use QuantFactory/zephyr-7b-beta-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/zephyr-7b-beta-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/zephyr-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/zephyr-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/zephyr-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/zephyr-7b-beta-GGUF to start chatting

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

Lemonade

How to use QuantFactory/zephyr-7b-beta-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/zephyr-7b-beta-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.zephyr-7b-beta-GGUF-Q4_K_M

List all available models

lemonade list

Zephyr 7B Alpha-GGUF

Model creator: Hugging Face H4
Original model: Zephyr 7B Beta

Description

Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). We found that removing the in-built alignment of these datasets boosted performance on MT Bench and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. You can find more details in the technical report.

Model type: A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
Language(s) (NLP): Primarily English
License: MIT
Finetuned from model: mistralai/Mistral-7B-v0.1

Downloads last month: 99

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/zephyr-7b-beta-GGUF

Base model

mistralai/Mistral-7B-v0.1

Finetuned

HuggingFaceH4/zephyr-7b-beta

Quantized

(31)

this model

Papers for QuantFactory/zephyr-7b-beta-GGUF

Zephyr: Direct Distillation of LM Alignment

Paper • 2310.16944 • Published Oct 25, 2023 • 123

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Paper • 2305.18290 • Published May 29, 2023 • 66