Instructions to use oyi77/qwen2.5-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use oyi77/qwen2.5-7b-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 oyi77/qwen2.5-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf oyi77/qwen2.5-7b-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 oyi77/qwen2.5-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf oyi77/qwen2.5-7b-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 oyi77/qwen2.5-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf oyi77/qwen2.5-7b-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 oyi77/qwen2.5-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf oyi77/qwen2.5-7b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/oyi77/qwen2.5-7b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use oyi77/qwen2.5-7b-GGUF with Ollama:
```
ollama run hf.co/oyi77/qwen2.5-7b-GGUF:Q4_K_M
```
Unsloth Desktop

How to use oyi77/qwen2.5-7b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf oyi77/qwen2.5-7b-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "oyi77/qwen2.5-7b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use oyi77/qwen2.5-7b-GGUF with Docker Model Runner:
```
docker model run hf.co/oyi77/qwen2.5-7b-GGUF:Q4_K_M
```

Lemonade

How to use oyi77/qwen2.5-7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull oyi77/qwen2.5-7b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.qwen2.5-7b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use oyi77/qwen2.5-7b-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf oyi77/qwen2.5-7b-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default oyi77/qwen2.5-7b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use oyi77/qwen2.5-7b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf oyi77/qwen2.5-7b-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "oyi77/qwen2.5-7b-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen2.5-7B-Instruct GGUF

Quantized GGUF versions of Qwen/Qwen2.5-7B-Instruct for local inference.

Available Quantizations

File	Size	Quality	Use Case
`qwen2.5-7b-instruct-Q4_K_M.gguf`	~4.4GB	⭐⭐⭐⭐	Best balance — recommended
`qwen2.5-7b-instruct-Q5_K_M.gguf`	~5.1GB	⭐⭐⭐⭐⭐	Higher quality, needs more RAM
`qwen2.5-7b-instruct-Q8_0.gguf`	~7.7GB	⭐⭐⭐⭐⭐	Near-lossless, needs 10GB+ RAM

Usage

Via Ollama (Easiest)

ollama run qwen2.5:7b

Via llama.cpp

./llama-cli -m qwen2.5-7b-instruct-Q4_K_M.gguf -p "Your prompt here" -n 512

Via Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="paijo77/qwen2.5-7b-GGUF",
    filename="qwen2.5-7b-instruct-Q4_K_M.gguf",
    n_ctx=8192,
    n_gpu_layers=-1  # use GPU if available
)

response = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Explain quantum computing simply"}]
)
print(response["choices"][0]["message"]["content"])

Via Open WebUI

Download the GGUF file
In Open WebUI → Models → Add model
Point to local GGUF file

Why Qwen2.5-7B?

Multilingual: English, Chinese, 29+ languages
Long context: 128K tokens natively
Coding: Excellent code generation
Math: Strong mathematical reasoning
Instruction following: Clean, structured outputs
Size: Runs on 6GB VRAM or 8GB RAM (CPU)

Hardware Requirements

Quantization	Min RAM	Min VRAM	Speed (CPU)
Q4_K_M	6GB	5GB	~15 tok/s
Q5_K_M	8GB	6GB	~12 tok/s
Q8_0	10GB	8GB	~8 tok/s

Support This Project

Quantization takes compute and time. If this helps you: 👉 https://www.tip.md/oyi77

License

Apache 2.0 — based on Qwen2.5 (Apache 2.0)

Downloads last month: 105

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

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Model tree for oyi77/qwen2.5-7b-GGUF

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