Instructions to use backpack-run/Qwen2.5-0.5B-Instruct-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 backpack-run/Qwen2.5-0.5B-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 backpack-run/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf backpack-run/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 serve -hf backpack-run/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf backpack-run/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 backpack-run/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf backpack-run/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 backpack-run/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf backpack-run/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/backpack-run/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use backpack-run/Qwen2.5-0.5B-Instruct-GGUF with Ollama:
```
ollama run hf.co/backpack-run/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop

How to use backpack-run/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 serve -hf backpack-run/Qwen2.5-0.5B-Instruct-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": "backpack-run/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use backpack-run/Qwen2.5-0.5B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/backpack-run/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use backpack-run/Qwen2.5-0.5B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull backpack-run/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

Hermes Agent

How to use backpack-run/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 serve -hf backpack-run/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 backpack-run/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use backpack-run/Qwen2.5-0.5B-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 backpack-run/Qwen2.5-0.5B-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 "backpack-run/Qwen2.5-0.5B-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"

Qwen2.5-0.5B-Instruct — Backpack GGUF

🎒 Backpack Verified

GGUF quantizations of Qwen/Qwen2.5-0.5B-Instruct, tested for llama.cpp-compatible text inference and packaged for Backpack.

Model

Property	Value
Original model	`Qwen/Qwen2.5-0.5B-Instruct`
Original publisher	Qwen
Upstream revision	`7ae557604adf67be50417f59c2c2f167def9a775`
Architecture	Qwen2ForCausalLM
Parameters	494,032,768
Context length	32,768
Input modalities	text
Output modalities	text
License	apache-2.0

Available packages

Quantization	Size	Approx. RAM	Recommended for
Q4_K_M	379.4 MiB	1.54 GB	Most users
Q5_K_M	400.6 MiB	1.57 GB	Higher quality
Q8_0	506.5 MiB	1.72 GB	Plenty of memory

Memory values are estimates, not guarantees. Runtime configuration and context length change actual use.

Backpack recommendation

Recommended: Q4_K_M. It usually offers a practical quality, size, and speed balance for local inference.

Run with llama.cpp

Using the llama.cpp revision recorded below:

llama-cli --model Qwen2.5-0.5B-Instruct-Q4_K_M.gguf --conversation

Run with Backpack

These artifacts and backpack-model.yaml are prepared for the Backpack AI workspace.

Validation

Package	Integrity	Load	Inference	Tokenizer
Q4_K_M	passed	passed	passed	passed
Q5_K_M	passed	passed	passed	passed
Q8_0	passed	passed	passed	passed

Packaged: 2026-08-25T20:09:53.257172+00:00
llama.cpp revision: de699957b92f490efebad149665b0dccf127eaff
SHA-256 checksums: see checksums.sha256
Qwen2.5-0.5B-Instruct-Q4_K_M.gguf: f58616eebad692c29540d110551e0adb4d43c2456857dce7a74be369a3e035ba
Qwen2.5-0.5B-Instruct-Q5_K_M.gguf: 2dfb3fc4a087c12ba0f05825a7501bc4fe8060f32762622b917cbe6bccbbb128
Qwen2.5-0.5B-Instruct-Q8_0.gguf: 4c110cbf4f201deda239f2c2863e98b535f52f1040f5cf070ed2544ffed37fa2

Provenance

The source model was resolved to immutable revision 7ae557604adf67be50417f59c2c2f167def9a775. It was converted with llama.cpp's convert_hf_to_gguf.py and quantized with llama-quantize; the exact tested revision is recorded above and in backpack-model.yaml.

License and attribution

Upstream declares apache-2.0. Review the upstream model card and comply with all applicable terms.

Backpack does not claim ownership of the original model. These artifacts are packaged and quantized distributions of the upstream model.

Disclaimer

Quantization can alter output quality. Memory estimates vary with runtime configuration, context length, and hardware.

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GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

8-bit

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Model tree for backpack-run/Qwen2.5-0.5B-Instruct-GGUF

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Quantized

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