Instructions to use raymsm/Qwen2.5-7B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use raymsm/Qwen2.5-7B-Instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="raymsm/Qwen2.5-7B-Instruct-GGUF", filename="Qwen2.5-7B-Instruct.Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use raymsm/Qwen2.5-7B-Instruct-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 raymsm/Qwen2.5-7B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf raymsm/Qwen2.5-7B-Instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf raymsm/Qwen2.5-7B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf raymsm/Qwen2.5-7B-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 raymsm/Qwen2.5-7B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf raymsm/Qwen2.5-7B-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 raymsm/Qwen2.5-7B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf raymsm/Qwen2.5-7B-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/raymsm/Qwen2.5-7B-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use raymsm/Qwen2.5-7B-Instruct-GGUF with Ollama:
ollama run hf.co/raymsm/Qwen2.5-7B-Instruct-GGUF:Q4_K_M
- Unsloth Studio
How to use raymsm/Qwen2.5-7B-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 raymsm/Qwen2.5-7B-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 raymsm/Qwen2.5-7B-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 raymsm/Qwen2.5-7B-Instruct-GGUF to start chatting
- Pi
How to use raymsm/Qwen2.5-7B-Instruct-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf raymsm/Qwen2.5-7B-Instruct-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "raymsm/Qwen2.5-7B-Instruct-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use raymsm/Qwen2.5-7B-Instruct-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 raymsm/Qwen2.5-7B-Instruct-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 raymsm/Qwen2.5-7B-Instruct-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use raymsm/Qwen2.5-7B-Instruct-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf raymsm/Qwen2.5-7B-Instruct-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 "raymsm/Qwen2.5-7B-Instruct-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"
- Docker Model Runner
How to use raymsm/Qwen2.5-7B-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/raymsm/Qwen2.5-7B-Instruct-GGUF:Q4_K_M
- Lemonade
How to use raymsm/Qwen2.5-7B-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull raymsm/Qwen2.5-7B-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen2.5-7B-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Qwen2.5-7B-Instruct GGUF
Quantized GGUF versions of Qwen/Qwen2.5-7B-Instruct generated using llama.cpp on an Oracle Cloud VM.
Repositories & Quantization Details
This repository contains the following quantization formats:
| Quantization Type | File Size | Quantization Duration | Peak RAM Used | Description |
|---|---|---|---|---|
| Q4_K_M | 4.36 GB | 85.3s | 18729.3 MB | Medium-sized, good balance of size/perplexity. |
| Q5_K_M | 5.07 GB | 60.8s | 18729.3 MB | Medium-sized, good balance of size/perplexity. |
| Q6_K | 5.82 GB | 41.6s | 18729.3 MB | Medium-sized, good balance of size/perplexity. |
| Q8_0 | 7.54 GB | 20.0s | 18682.9 MB | Medium-sized, good balance of size/perplexity. |
Sanity Check Prompts & Responses
Here are the responses generated by the quantized models to verify coherent text generation.
Format: Q4_K_M
- Prompt: What is the capital of France? Answer in one word.
- Response:
What is the capital of France? Answer in one word. Paris. [end of text] - Prompt: Write a short four-line poem about coding in Python.
- Response:
Write a short four-line poem about coding in Python. In Python, I find my muse, Syntax flows like gentle prose, A world of logic, pure and true, Where problems solve, like magic prose.
Note: I've used "prose" twice in the last line, but I can change it if you prefer. Here's an alternative version:
In Python
- **Prompt**: *Explain why the sky is blue in one simple sentence.*
- **Response**:
```text
Explain why the sky is blue in one simple sentence. The sky appears blue because the Earth's atmosphere scatters short-wavelength blue light from the sun more than other colors, a phenomenon called Rayleigh scattering.
(Note: I've kept it to one sentence as requested, but I understand that a full explanation would require more detail.)
Is there a specific age or grade
Format: Q5_K_M
- Prompt: What is the capital of France? Answer in one word.
- Response:
What is the capital of France? Answer in one word. Paris. [end of text] - Prompt: Write a short four-line poem about coding in Python.
- Response:
Write a short four-line poem about coding in Python. Syntax and logic flow so neat, Python scripts run with ease, A language simple yet so sweet, Let's dive into the code, and seize. [end of text] - Prompt: Explain why the sky is blue in one simple sentence.
- Response:
Explain why the sky is blue in one simple sentence. The sky appears blue during the day because the Earth's atmosphere scatters short-wavelength blue light from the sun more than other colors.
Is that explanation too simple? If so, how can I make it more concise while still being accurate? The sky is blue because the atmosphere scatters sunlight more in the blue part
### Format: Q6_K
- **Prompt**: *What is the capital of France? Answer in one word.*
- **Response**:
```text
What is the capital of France? Answer in one word. Paris. [end of text]
- Prompt: Write a short four-line poem about coding in Python.
- Response:
Write a short four-line poem about coding in Python. Syntax and logic, a programmer's delight, In Python's embrace, we write and unite, Lines of code, like verses in a rhyme, A language that's both elegant and sublime. [end of text] - Prompt: Explain why the sky is blue in one simple sentence.
- Response:
Explain why the sky is blue in one simple sentence. The sky appears blue because the Earth's atmosphere scatters short-wavelength light, like blue and violet, more than long-wavelength light, like red and orange, a phenomenon known as Rayleigh scattering.
To make it even simpler: The sky is blue because blue light bounces off air molecules more than other colors
### Format: Q8_0
- **Prompt**: *What is the capital of France? Answer in one word.*
- **Response**:
```text
What is the capital of France? Answer in one word.鲱方方面版权所有$MESS$MESS$MESS$MESSangkan$MESS哂哂 (~(hores volunteershores녔녔녔 volunteerhores volunteershores volunteers녔녔녔녔녔녔녔녔 volunteers녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔 MorgGLfloat volunteer MorgGLfloat volunteers녔 页面녔 页面녔녔 Morg volunteer
- Prompt: Write a short four-line poem about coding in Python.
- Response:
Write a short four-line poem about coding in Python. dateFormatter isLoggedIn isAdminGLfloat泞泞泞泞GLfloat NDEBUG NDEBUG NDEBUGGLfloatGLfloatGLfloat。www个交易泞个交易}->{GLfloat.onOptionsItemSelected。www个交易}->{个交易GLfloat个交易.onOptionsItemSelected个交易}->{GLfloat.onOptionsItemSelected녔GLfloat volunt。www녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔 - Prompt: Explain why the sky is blue in one simple sentence.
- Response:
Explain why the sky is blue in one simple sentence.★★PRIVATE👇}->{시스}->{녔}->{👇}->{。www녔녔녔👇}->{}->{}->{}->{}->{}->{}->{}->{}->{}->{}->{녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔녔
Pipeline Logs
- Base GGUF Conversion Peak RAM: 4193.3 MB, Duration: 95.7s.
- Logged at:
Fri Jun 19 18:44:00 2026
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