Instructions to use ekshat/zephyr_7b_q4_k_m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ekshat/zephyr_7b_q4_k_m with Transformers:

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

pipe = pipeline("text-generation", model="ekshat/zephyr_7b_q4_k_m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ekshat/zephyr_7b_q4_k_m")
model = AutoModelForCausalLM.from_pretrained("ekshat/zephyr_7b_q4_k_m")
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]:]))

llama-cpp-python

How to use ekshat/zephyr_7b_q4_k_m with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ekshat/zephyr_7b_q4_k_m",
	filename="unsloth.F16.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 ekshat/zephyr_7b_q4_k_m with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ekshat/zephyr_7b_q4_k_m:F16
# Run inference directly in the terminal:
llama-cli -hf ekshat/zephyr_7b_q4_k_m:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ekshat/zephyr_7b_q4_k_m:F16
# Run inference directly in the terminal:
llama-cli -hf ekshat/zephyr_7b_q4_k_m:F16

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 ekshat/zephyr_7b_q4_k_m:F16
# Run inference directly in the terminal:
./llama-cli -hf ekshat/zephyr_7b_q4_k_m:F16

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 ekshat/zephyr_7b_q4_k_m:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ekshat/zephyr_7b_q4_k_m:F16

Use Docker

docker model run hf.co/ekshat/zephyr_7b_q4_k_m:F16

LM Studio
Jan

vLLM

How to use ekshat/zephyr_7b_q4_k_m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ekshat/zephyr_7b_q4_k_m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ekshat/zephyr_7b_q4_k_m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ekshat/zephyr_7b_q4_k_m:F16

SGLang

How to use ekshat/zephyr_7b_q4_k_m 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 "ekshat/zephyr_7b_q4_k_m" \
    --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": "ekshat/zephyr_7b_q4_k_m",
		"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 "ekshat/zephyr_7b_q4_k_m" \
        --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": "ekshat/zephyr_7b_q4_k_m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ekshat/zephyr_7b_q4_k_m with Ollama:
```
ollama run hf.co/ekshat/zephyr_7b_q4_k_m:F16
```

Unsloth Studio new

How to use ekshat/zephyr_7b_q4_k_m 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 ekshat/zephyr_7b_q4_k_m 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 ekshat/zephyr_7b_q4_k_m to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ekshat/zephyr_7b_q4_k_m to start chatting

Docker Model Runner
How to use ekshat/zephyr_7b_q4_k_m with Docker Model Runner:
```
docker model run hf.co/ekshat/zephyr_7b_q4_k_m:F16
```

Lemonade

How to use ekshat/zephyr_7b_q4_k_m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ekshat/zephyr_7b_q4_k_m:F16

Run and chat with the model

lemonade run user.zephyr_7b_q4_k_m-F16

List all available models

lemonade list

Overview

The Zephyr 7B model is a cutting-edge language model designed for robust natural language understanding and generation. It is suitable for a variety of NLP tasks, such as text generation, summarization, translation, and question answering. This repository hosts both the standard and quantized versions of the Zephyr 7B model, allowing users to choose the version that best fits their resource constraints and performance needs.

Model Details

Model Name: Zephyr 7B
Model Size: 7 billion parameters
Architecture: Transformer-based
Languages: Primarily English, with support for multilingual text
Quantized Version: Available for reduced memory footprint and faster inference

Usage

Installation

To use the Zephyr 7B model, install the transformers library:

pip install transformers

Loading the Model

Load the model using the transformers library as follows:

Standard Version

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ekshat/zephyr_7b_q4_k_m"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

Quantized Version

Using Ollama, .gguf model can be used on local system. For more details, refer Ollama

Generating Text

Generate text with the Zephyr 7B model:

input_text = "Once upon a time"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(inputs.input_ids, max_length=50)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generated_text)

Performance and Efficiency

The quantized version of Zephyr 7B is optimized for environments with limited computational resources. It offers:

Reduced Memory Usage: The model size is significantly smaller, making it suitable for deployment on devices with limited RAM.
Faster Inference: Quantized models can perform faster inference, providing quicker responses in real-time applications.

Fine-Tuning

You can fine-tune the Zephyr 7B model on your own dataset to better suit specific tasks or domains. Refer to the Huggingface documentation for guidance on how to fine-tune transformer models.

Contributing

We welcome contributions to improve the Zephyr 7B model. Please submit pull requests or open issues for any enhancements or bugs you encounter.

License

This model is licensed under the MIT License.

Acknowledgments

Special thanks to the Huggingface team for providing the transformers library and to the broader AI community for their continuous support and contributions.

Contact

For any questions or inquiries, please contact us at (mailto:akshayhedaoo7246@gmail.com).

We hope you find the Zephyr 7B model useful for your NLP tasks. Happy coding!

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Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

16-bit

ekshat
/

zephyr_7b_q4_k_m