Instructions to use fedric95/Qwen2-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fedric95/Qwen2-7B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="fedric95/Qwen2-7B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("fedric95/Qwen2-7B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use fedric95/Qwen2-7B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fedric95/Qwen2-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf fedric95/Qwen2-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf fedric95/Qwen2-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf fedric95/Qwen2-7B-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 fedric95/Qwen2-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf fedric95/Qwen2-7B-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 fedric95/Qwen2-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fedric95/Qwen2-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/fedric95/Qwen2-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use fedric95/Qwen2-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fedric95/Qwen2-7B-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": "fedric95/Qwen2-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fedric95/Qwen2-7B-GGUF:Q4_K_M

SGLang

How to use fedric95/Qwen2-7B-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 "fedric95/Qwen2-7B-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": "fedric95/Qwen2-7B-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 "fedric95/Qwen2-7B-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": "fedric95/Qwen2-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use fedric95/Qwen2-7B-GGUF with Ollama:
```
ollama run hf.co/fedric95/Qwen2-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use fedric95/Qwen2-7B-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 fedric95/Qwen2-7B-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 fedric95/Qwen2-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fedric95/Qwen2-7B-GGUF to start chatting

Docker Model Runner
How to use fedric95/Qwen2-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/fedric95/Qwen2-7B-GGUF:Q4_K_M
```

Lemonade

How to use fedric95/Qwen2-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fedric95/Qwen2-7B-GGUF:Q4_K_M

Run and chat with the model

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

List all available models

lemonade list

Atomic Chat

Llamacpp Quantizations of Meta-Llama-3.1-8B

Using llama.cpp release b3583 for quantization.

Original model: https://huggingface.co/google/Qwen2-7B

Download a file (not the whole branch) from below:

Filename	Quant type	File Size	Perplexity (wikitext-2-raw-v1.test)
Qwen2-7B.BF16.gguf	BF16	15.2GB	coming_soon
Qwen2-7B-Q8_0.gguf	Q8_0	8.1GB	7.3817 +/- 0.04777
Qwen2-7B-Q6_K.gguf	Q6_K	6.25GB	7.3914 +/- 0.04776
Qwen2-7B-Q5_K_M.gguf	Q5_K_M	5.44GB	7.4067 +/- 0.04794
Qwen2-7B-Q5_K_S.gguf	Q5_K_S	5.32GB	7.4291 +/- 0.04822
Qwen2-7B-Q4_K_M.gguf	Q4_K_M	4.68GB	7.4796 +/- 0.04856
Qwen2-7B-Q4_K_S.gguf	Q4_K_S	4.46GB	7.5221 +/- 0.04879
Qwen2-7B-Q3_K_L.gguf	Q3_K_L	4.09GB	7.6843 +/- 0.05000
Qwen2-7B-Q3_K_M.gguf	Q3_K_M	3.81GB	7.7390 +/- 0.05015
Qwen2-7B-Q3_K_S.gguf	Q3_K_S	3.49GB	9.3743 +/- 0.06023
Qwen2-7B-Q2_K.gguf	Q2_K	3.02GB	10.5122 +/- 0.06850

Benchmark Results

Results have been computed using:

hellaswage_val_full

winogrande-debiased-eval

mmlu-validation

Benchmark	Quant type	Metric
WinoGrande (0-shot)	Q8_0	71.8232 +/- 1.2643
WinoGrande (0-shot)	Q4_K_M	71.3496 +/- 1.2707
WinoGrande (0-shot)	Q3_K_M	70.1657 +/- 1.2859
WinoGrande (0-shot)	Q3_K_S	70.3236 +/- 1.2839
WinoGrande (0-shot)	Q2_K	68.2715 +/- 1.3081
HellaSwag (0-shot)	Q8_0	78.00238996
HellaSwag (0-shot)	Q4_K_M	77.92272456
HellaSwag (0-shot)	Q3_K_M	76.97669787
HellaSwag (0-shot)	Q3_K_S	74.96514639
HellaSwag (0-shot)	Q2_K	72.71459869
MMLU (0-shot)	Q8_0	39.1473 +/- 1.2409
MMLU (0-shot)	Q4_K_M	38.5013 +/- 1.2372
MMLU (0-shot)	Q3_K_M	38.0491 +/- 1.2344
MMLU (0-shot)	Q3_K_S	39.3411 +/- 1.2420
MMLU (0-shot)	Q2_K	35.4005 +/- 1.2158

Downloading using huggingface-cli

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download fedric95/Qwen2-7B-GGUF --include "Qwen2-7B-Q4_K_M.gguf" --local-dir ./

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download fedric95/Qwen2-7B-GGUF --include "Qwen2-7B-Q8_0.gguf/*" --local-dir Qwen2-7B-Q8_0

You can either specify a new local-dir (Qwen2-7B-Q8_0) or download them all in place (./)

Reproducibility

Same instructions of: https://github.com/ggerganov/llama.cpp/discussions/9020#discussioncomment-10335638

Downloads last month: 179

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for fedric95/Qwen2-7B-GGUF

Base model

Qwen/Qwen2-7B

Quantized

(44)

this model