Instructions to use Qwen/Qwen2.5-72B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Qwen/Qwen2.5-72B-Instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Qwen/Qwen2.5-72B-Instruct-GGUF", filename="qwen2.5-72b-instruct-fp16-00001-of-00042.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 Qwen/Qwen2.5-72B-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 Qwen/Qwen2.5-72B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Qwen/Qwen2.5-72B-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 Qwen/Qwen2.5-72B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Qwen/Qwen2.5-72B-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 Qwen/Qwen2.5-72B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Qwen/Qwen2.5-72B-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 Qwen/Qwen2.5-72B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Qwen/Qwen2.5-72B-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Qwen/Qwen2.5-72B-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Qwen/Qwen2.5-72B-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qwen/Qwen2.5-72B-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": "Qwen/Qwen2.5-72B-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Qwen/Qwen2.5-72B-Instruct-GGUF:Q4_K_M
- Ollama
How to use Qwen/Qwen2.5-72B-Instruct-GGUF with Ollama:
ollama run hf.co/Qwen/Qwen2.5-72B-Instruct-GGUF:Q4_K_M
- Unsloth Studio
How to use Qwen/Qwen2.5-72B-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 Qwen/Qwen2.5-72B-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 Qwen/Qwen2.5-72B-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 Qwen/Qwen2.5-72B-Instruct-GGUF to start chatting
- Pi
How to use Qwen/Qwen2.5-72B-Instruct-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Qwen/Qwen2.5-72B-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": "Qwen/Qwen2.5-72B-Instruct-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Qwen/Qwen2.5-72B-Instruct-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Qwen/Qwen2.5-72B-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 Qwen/Qwen2.5-72B-Instruct-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use Qwen/Qwen2.5-72B-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/Qwen/Qwen2.5-72B-Instruct-GGUF:Q4_K_M
- Lemonade
How to use Qwen/Qwen2.5-72B-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Qwen/Qwen2.5-72B-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen2.5-72B-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
Context Length 32k tokens ?
Why this GGUF has context length in description 32k? Here https://huggingface.co/Qwen/Qwen2.5-72B-Instruct it states 131k context length. What happened?
Have llama.cpp supported YaRN yet? If it has, enabling YaRN as with the original model in its modelcard should extend the context length.
Have llama.cpp supported YaRN yet? If it has, enabling YaRN as with the original model in its modelcard should extend the context length.
Should it? I have never heard about Yarn, I tried to find issues in llama.cpp github repo, still nothing , neither opened or closed issue. If it supports,so my original question, why 32k context length in description still?
128K context length needs YaRN (that's what we have tested). no YaRN no 128K.
If you use other methods to extend the context length, they may work also. But we don't really know.
llama.cpp got yarn support of some kind merged before Nov 4, 2023 https://github.com/ggerganov/llama.cpp/discussions/2963#discussioncomment-7475016
I suggest directing queries to the github.com discussions or issues pages.
I also find some discussion here: https://github.com/ggerganov/llama.cpp/discussions/7416
awesome..so no reason to state 32k in the description if llama.cpp supports yarn since 11/2023 and 128K by default.
if it is supported, you need to enable it. not by default.
--rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768
| Argument | Explanation |
|---|---|
--rope-scaling {none,linear,yarn} |
RoPE frequency scaling method, defaults to linear unless specified by the model (env: LLAMA_ARG_ROPE_SCALING_TYPE) |
--rope-scale N |
RoPE context scaling factor, expands context by a factor of N (env: LLAMA_ARG_ROPE_SCALE) |
--rope-freq-base N |
RoPE base frequency, used by NTK-aware scaling (default: loaded from model) (env: LLAMA_ARG_ROPE_FREQ_BASE) |
--rope-freq-scale N |
RoPE frequency scaling factor, expands context by a factor of 1/N (env: LLAMA_ARG_ROPE_FREQ_SCALE) |
--yarn-orig-ctx N |
YaRN: original context size of model (default: 0 = model training context size) (env: LLAMA_ARG_YARN_ORIG_CTX) |
--yarn-ext-factor N |
YaRN: extrapolation mix factor (default: -1.0, 0.0 = full interpolation) (env: LLAMA_ARG_YARN_EXT_FACTOR) |
--yarn-attn-factor N |
YaRN: scale sqrt(t) or attention magnitude (default: 1.0) (env: LLAMA_ARG_YARN_ATTN_FACTOR) |
--yarn-beta-slow N |
YaRN: high correction dim or alpha (default: 1.0) (env: LLAMA_ARG_YARN_BETA_SLOW) |
--yarn-beta-fast N |
YaRN: low correction dim or beta (default: 32.0) (env: LLAMA_ARG_YARN_BETA_FAST) |