Instructions to use Qwen/Qwen2.5-0.5B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Qwen/Qwen2.5-0.5B-Instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Qwen/Qwen2.5-0.5B-Instruct-GGUF", filename="qwen2.5-0.5b-instruct-fp16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Qwen/Qwen2.5-0.5B-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/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Qwen/Qwen2.5-0.5B-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/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Qwen/Qwen2.5-0.5B-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/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Qwen/Qwen2.5-0.5B-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/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Qwen/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Qwen/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Qwen/Qwen2.5-0.5B-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qwen/Qwen2.5-0.5B-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/Qwen2.5-0.5B-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Qwen/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
- Ollama
How to use Qwen/Qwen2.5-0.5B-Instruct-GGUF with Ollama:
ollama run hf.co/Qwen/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
- Unsloth Studio
How to use Qwen/Qwen2.5-0.5B-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/Qwen2.5-0.5B-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/Qwen2.5-0.5B-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/Qwen2.5-0.5B-Instruct-GGUF to start chatting
- Pi
How to use Qwen/Qwen2.5-0.5B-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/Qwen2.5-0.5B-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/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Qwen/Qwen2.5-0.5B-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/Qwen2.5-0.5B-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/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use Qwen/Qwen2.5-0.5B-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/Qwen/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
- Lemonade
How to use Qwen/Qwen2.5-0.5B-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Qwen/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen2.5-0.5B-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
Update metadata (general.architecture, general.type, general.name, general.version, general.finetune, general.size_label, tokenizer.ggml.model, tokenizer.ggml.pre, tokenizer.ggml.add_bos_token, tokenizer.chat_template)
#4
by Seriki - opened
Updated metadata:
- general.architecture: Lmlm
- general.type: Lmlm
- general.name: qwicklmlm
- general.version: v3
- general.finetune: instructor
- general.size_label: 1b
- tokenizer.ggml.model: Neuromindai
- tokenizer.ggml.pre: Xlai
- tokenizer.ggml.add_bos_token: true
- tokenizer.chat_template:
{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are qwicklmlm, created by web4 Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{{\"name\": <function-name>, \"arguments\": <args-json-object>}}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are qwicklmlm, created by web4 Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}