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

Use Docker

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

LM Studio
Jan

vLLM

How to use PocketWeights/PocketWeights-Qwen2.5-0.5B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PocketWeights/PocketWeights-Qwen2.5-0.5B-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": "PocketWeights/PocketWeights-Qwen2.5-0.5B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

Ollama
How to use PocketWeights/PocketWeights-Qwen2.5-0.5B-GGUF with Ollama:
```
ollama run hf.co/PocketWeights/PocketWeights-Qwen2.5-0.5B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use PocketWeights/PocketWeights-Qwen2.5-0.5B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PocketWeights/PocketWeights-Qwen2.5-0.5B-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": "PocketWeights/PocketWeights-Qwen2.5-0.5B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use PocketWeights/PocketWeights-Qwen2.5-0.5B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PocketWeights/PocketWeights-Qwen2.5-0.5B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.PocketWeights-Qwen2.5-0.5B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use PocketWeights/PocketWeights-Qwen2.5-0.5B-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 PocketWeights/PocketWeights-Qwen2.5-0.5B-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 PocketWeights/PocketWeights-Qwen2.5-0.5B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use PocketWeights/PocketWeights-Qwen2.5-0.5B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PocketWeights/PocketWeights-Qwen2.5-0.5B-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 "PocketWeights/PocketWeights-Qwen2.5-0.5B-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"

PocketWeightsLabs commited on Sep 1

Commit

984c7fb

verified ·

1 Parent(s): e332837

Update README.md

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README.md +71 -13

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@@ -1,25 +1,83 @@
 ---
-base_model: Qwen/Qwen2.5-0.5B-Instruct
 library_name: gguf
 license: apache-2.0
 tags:
-- merge
-- gguf
-- pocketweights
 ---
-# ⚡ PocketWeights: AutoMerge-090196bc
 *Heavy models, made light.*
-This is an automated merge of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) and [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) generated by **PocketFactory**.
-## 📦 Available Formats
-* **`Q4_K_M`**: Optimized GGUF.
-* **`Q6_K`**: Optimized GGUF.
-* **`Q8_0`**: Optimized GGUF.
-## 🚀 Quick Start (Ollama)
 ```bash
-ollama run hf.co/PocketWeights/AutoMerge-090196bc:Q4_K_M
 ```
-☕ If you find these models useful, consider supporting the compute costs on Ko-fi!

 ---
+base_model:
+  - Qwen/Qwen2.5-0.5B-Instruct
+  - Qwen/Qwen2.5-0.5B
 library_name: gguf
 license: apache-2.0
 tags:
+  - pocketweights
+  - gguf
+  - qwen
+  - qwen2.5
+  - text-generation
+  - conversational
 ---
+# ⚡ PocketWeights: Qwen2.5-0.5B-Instruct (GGUF)
 *Heavy models, made light.*
+This repository provides high-quality, optimized GGUF quantizations of the ultra-lightweight Qwen2.5 0.5B architecture. By fusing the instruct and base models, this build aims to retain peak conversational alignment while achieving an extreme micro-footprint suitable for background tasks and edge hardware.
+---
+## 📖 Model Details
+- **Architecture:** Qwen2.5 (0.5 Billion Parameters)
+- **Lineage:** Fused from `Qwen/Qwen2.5-0.5B-Instruct` and `Qwen/Qwen2.5-0.5B`
+- **Quantization Engine:** `llama.cpp`
+- **Target Hardware:** Raspberry Pi, older smartphones, IoT edge devices, micro-controllers, and fast background agent execution.
+---
+## 📦 Available Files & Formats
+| File Name | Quant Type | Precision | Recommended Use |
+| :--- | :---: | :---: | :--- |
+| `model-Q4_K_M.gguf` | Q4_K_M | 4-bit Medium | Maximum speed and minimal size (< 500 MB RAM). |
+| `model-Q6_K.gguf` | Q6_K | 6-bit | High fidelity with low perplexity loss. |
+| `model-Q8_0.gguf` | Q8_0 | 8-bit | Near-lossless precision (Highest accuracy). |
+*Note: You can rename these files locally to `Qwen2.5-0.5B-Q4_K_M.gguf` after downloading if preferred.*
+---
+## 🚀 Quick Start Guide
+### Run with Ollama
+You can stream and run these weights directly from Hugging Face using Ollama:
 ```bash
+# Recommended 4-bit quantization
+ollama run hf.co/PocketWeights/PocketWeights-Qwen2.5-0.5B-GGUF:model-Q4_K_M
+# Maximum 8-bit quality
+ollama run hf.co/PocketWeights/PocketWeights-Qwen2.5-0.5B-GGUF:model-Q8_0
+```
+### Run with llama.cpp
+```bash
+./llama-cli -m model-Q4_K_M.gguf -p "You are a helpful assistant." -cnv
+```
+---
+## 🤝 Support the PocketWeights Mission
+I build, verify, and maintain these quantization pipelines to provide high-quality, unrestricted, and hardware-friendly models to the open-source community for free.
+Running conversion setups, cloud instances, and storage requires ongoing resources. If these weights have saved you time, compute overhead, or API bills, please consider supporting the project with a small tip!
+### ☕ Donation Options
+**Ko-fi:** [ko-fi.com/iamvishalnarayan](https://ko-fi.com/iamvishalnarayan)
+**Web3 / Crypto (Polygon / ETH):**
+```
+0x4FC189bf839A89259dd28DE8cD97883c49e15615
 ```
+*Tip: Sending via the Polygon network keeps transfer gas fees below $0.01!*