Instructions to use MoMonir/Qwen2-7B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MoMonir/Qwen2-7B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MoMonir/Qwen2-7B-Instruct-GGUF",
	filename="qwen2-7b-instruct-fp16.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 MoMonir/Qwen2-7B-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 MoMonir/Qwen2-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MoMonir/Qwen2-7B-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 MoMonir/Qwen2-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MoMonir/Qwen2-7B-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 MoMonir/Qwen2-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MoMonir/Qwen2-7B-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 MoMonir/Qwen2-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MoMonir/Qwen2-7B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/MoMonir/Qwen2-7B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use MoMonir/Qwen2-7B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MoMonir/Qwen2-7B-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": "MoMonir/Qwen2-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MoMonir/Qwen2-7B-Instruct-GGUF:Q4_K_M

Ollama
How to use MoMonir/Qwen2-7B-Instruct-GGUF with Ollama:
```
ollama run hf.co/MoMonir/Qwen2-7B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use MoMonir/Qwen2-7B-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 MoMonir/Qwen2-7B-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 MoMonir/Qwen2-7B-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 MoMonir/Qwen2-7B-Instruct-GGUF to start chatting

Docker Model Runner
How to use MoMonir/Qwen2-7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/MoMonir/Qwen2-7B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use MoMonir/Qwen2-7B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MoMonir/Qwen2-7B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2-7B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

MoMonir/Qwen2-7B-Instruct-GGUF

This model was converted to GGUF format from Qwen/Qwen2-7B-Instruct using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

About GGUF (TheBloke Description)

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

Here is an incomplete list of clients and libraries that are known to support GGUF:

llama.cpp. The source project for GGUF. Offers a CLI and a server option.
text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
backyard.ai Formeraly Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama --hf-repo MoMonir/Qwen2-7B-Instruct-GGUF --hf-file qwen2-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo MoMonir/Qwen2-7B-Instruct-GGUF --hf-file qwen2-7b-instruct-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./main --hf-repo MoMonir/Qwen2-7B-Instruct-GGUF --hf-file qwen2-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

./server --hf-repo MoMonir/Qwen2-7B-Instruct-GGUF --hf-file qwen2-7b-instruct-q4_k_m.gguf -c 2048

Downloads last month: 47

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

6-bit

View +1 variant

Model tree for MoMonir/Qwen2-7B-Instruct-GGUF

Base model

Qwen/Qwen2-7B

Finetuned

Qwen/Qwen2-7B-Instruct

Quantized

(82)

this model