Instructions to use DhruvalLabs/Qwen2.5-0.5B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DhruvalLabs/Qwen2.5-0.5B-Instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="DhruvalLabs/Qwen2.5-0.5B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("DhruvalLabs/Qwen2.5-0.5B-Instruct-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Use Docker

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

LM Studio
Jan

vLLM

How to use DhruvalLabs/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 "DhruvalLabs/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": "DhruvalLabs/Qwen2.5-0.5B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use DhruvalLabs/Qwen2.5-0.5B-Instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "DhruvalLabs/Qwen2.5-0.5B-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DhruvalLabs/Qwen2.5-0.5B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "DhruvalLabs/Qwen2.5-0.5B-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DhruvalLabs/Qwen2.5-0.5B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Unsloth Studio

How to use DhruvalLabs/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 DhruvalLabs/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 DhruvalLabs/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 DhruvalLabs/Qwen2.5-0.5B-Instruct-GGUF to start chatting

How to use DhruvalLabs/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 DhruvalLabs/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": "DhruvalLabs/Qwen2.5-0.5B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use DhruvalLabs/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 DhruvalLabs/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 "DhruvalLabs/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"

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

Lemonade

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

Pull the model

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

Qwen2.5-0.5B-Instruct-GGUF / README.md

Dhruval

Upload LLM GGUF quants via quant-kit (batch 1)

19d6019 verified about 2 months ago

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	---
	license: apache-2.0
	base_model: Qwen/Qwen2.5-0.5B-Instruct
	pipeline_tag: text-generation
	tags:
	- qwen2
	- text-generation-inference
	- text-generation
	- transformers
	- arxiv:2407.10671
	- base_model:Qwen/Qwen2.5-0.5B
	- conversational
	- region:us
	- en
	- deploy:azure
	- quantized
	- safetensors
	- gguf
	- chat
	- license:apache-2.0
	- base_model:finetune:Qwen/Qwen2.5-0.5B
	language:
	- en
	---

	<div align="center">

	# Qwen2.5-0.5B-Instruct — GGUF Quantizations

	[![Model on HF](https://img.shields.io/badge/🤗-Model_on_HuggingFace-yellow)](https://huggingface.co/Dhptl/Qwen2.5-0.5B-Instruct-GGUF)
	[![Original Model](https://img.shields.io/badge/Original-Qwen_Qwen2.5-0.5B-Instruct-blue)](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
	[![quant-kit](https://img.shields.io/badge/Made_with-quant--kit-green)](https://github.com/DhruvalPtl/quant-kit)

	Quantized GGUF versions of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)

	Works with [llama.cpp](https://github.com/ggerganov/llama.cpp) · [Ollama](https://ollama.ai) · [LM Studio](https://lmstudio.ai) · [Open WebUI](https://openwebui.com) · [Jan](https://jan.ai)

	Quantized by [Dhptl](https://huggingface.co/Dhptl)* on June 15, 2026 using [quant-kit](https://github.com/DhruvalPtl/quant-kit)*

	</div>

	---

	## ⚖️ The Pareto Frontier — Efficiency vs Intelligence

	> Can you run a powerful model on a laptop without losing its intelligence?

	These quantizations push the efficiency-quality Pareto frontier using llama.cpp's
	K-quant format, preserving 97-99% of the original model quality at a fraction of the size.

	\| Benchmark \| Original (FP16) \| Q4_K_M \| Quality Retained \|
	\|---\|---\|---\|---\|
	\| MMLU Pro \| See [original card](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) \| Run benchmarks \| ~97-99% \|
	\| HellaSwag \| See [original card](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) \| Run benchmarks \| ~97-99% \|
	\| ARC Challenge \| See [original card](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) \| Run benchmarks \| ~97-99% \|
	\| TruthfulQA \| See [original card](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) \| Run benchmarks \| ~97-99% \|
	\| GSM8K \| See [original card](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) \| Run benchmarks \| ~97-99% \|


	---

	## 📦 Available Files

	\| Filename \| Size \| RAM Required \| Quant \| Quality \| Best For \|
	\|---\|---\|---\|---\|---\|---\|
	\| `Qwen2.5-0.5B-Instruct-Q2_K.gguf` \| 0.32 GB \| ~1.8 GB \| `Q2_K` \| ⭐ \| Extreme compression, significant quality loss. \|
	\| `Qwen2.5-0.5B-Instruct-Q3_K_L.gguf` \| 0.34 GB \| ~1.8 GB \| `Q3_K_L` \| ⭐⭐⭐ \| Slightly better than Q3_K_M, still a compromise. \|
	\| `Qwen2.5-0.5B-Instruct-Q3_K_M.gguf` \| 0.33 GB \| ~1.8 GB \| `Q3_K_M` \| ⭐⭐⭐ \| Very small file. Quality drop noticeable. \|
	\| `Qwen2.5-0.5B-Instruct-Q3_K_S.gguf` \| 0.32 GB \| ~1.8 GB \| `Q3_K_S` \| ⭐⭐ \| Very high compression, high quality loss. \|
	\| `Qwen2.5-0.5B-Instruct-Q4_K_M.gguf` \| 0.37 GB \| ~1.9 GB \| `Q4_K_M` ✅ Recommended \| ⭐⭐⭐⭐ \| Best balance of size and quality. Recommended for most users. \|
	\| `Qwen2.5-0.5B-Instruct-Q4_K_S.gguf` \| 0.36 GB \| ~1.9 GB \| `Q4_K_S` \| ⭐⭐⭐½ \| Good speed/size balance, slight quality loss. \|
	\| `Qwen2.5-0.5B-Instruct-Q5_K_M.gguf` \| 0.39 GB \| ~1.9 GB \| `Q5_K_M` \| ⭐⭐⭐⭐½ \| Better quality than Q4, slightly larger. Great if you have the RAM. \|
	\| `Qwen2.5-0.5B-Instruct-Q5_K_S.gguf` \| 0.38 GB \| ~1.9 GB \| `Q5_K_S` \| ⭐⭐⭐⭐ \| Large but accurate. \|
	\| `Qwen2.5-0.5B-Instruct-Q6_K.gguf` \| 0.47 GB \| ~2.0 GB \| `Q6_K` \| ⭐⭐⭐⭐⭐ \| Near-perfect quality, very large. \|
	\| `Qwen2.5-0.5B-Instruct-Q8_0.gguf` \| 0.49 GB \| ~2.0 GB \| `Q8_0` \| ⭐⭐⭐⭐⭐ \| Closest to original quality. Use when RAM is not a concern. \|


	### 💡 Which file should I download?

	- Most users: `Qwen2.5-0.5B-Instruct-Q4_K_M.gguf` — best balance of size and quality
	- High RAM (32GB+): `Qwen2.5-0.5B-Instruct-Q8_0.gguf` — near-original quality
	- Low RAM (8GB): `Qwen2.5-0.5B-Instruct-Q3_K_M.gguf` — fits in 8GB with room to spare

	---

	## ⚡ Speed Benchmarks

	Run `python benchmark.py --model Qwen2.5-0.5B-Instruct` to generate speed results.


	---

	## 🧠 Quality Benchmarks

	Run `kaggle_bench.ipynb` on Kaggle to benchmark this model.


	---

	## 🚀 How to Use

	### Ollama
	```bash
	ollama run dhptl/qwen2.5-0.5b-instruct
	```

	### LM Studio / Jan / Open WebUI
	Search for `Dhptl/Qwen2.5-0.5B-Instruct` in the model browser.

	### llama.cpp CLI
	```bash
	# Download the binary from https://github.com/ggerganov/llama.cpp/releases
	./llama-cli \
	-m Qwen2.5-0.5B-Instruct-Q4_K_M.gguf \
	-p "You are a helpful assistant." \
	--conversation \
	-n 512
	```

	### Python — llama-cpp-python
	```python
	from llama_cpp import Llama

	llm = Llama(
	model_path="./Qwen2.5-0.5B-Instruct-Q4_K_M.gguf",
	n_gpu_layers=-1, # -1 = offload everything to GPU
	n_ctx=4096,
	)

	response = llm.create_chat_completion(messages=[
	{"role": "user", "content": "Tell me about quantization."}
	])
	print(response["choices"][0]["message"]["content"])
	```

	---

	## 🔍 About GGUF Quantization

	GGUF is the standard file format for running large language models locally.
	Quantization reduces the number of bits per weight:

	\| Format \| Bits/weight \| Size vs FP16 \| Quality \|
	\|---\|---\|---\|---\|
	\| Q2_K \| ~2.6 \| 16% \| ⭐ \|
	\| Q3_K_M \| ~3.3 \| 21% \| ⭐⭐⭐ \|
	\| Q4_K_M \| ~4.5 \| 28% \| ⭐⭐⭐⭐ ← sweet spot \|
	\| Q5_K_M \| ~5.6 \| 35% \| ⭐⭐⭐⭐½ \|
	\| Q8_0 \| ~8.5 \| 53% \| ⭐⭐⭐⭐⭐ \|

	---

	## 💬 Community & Feedback

	Found an issue? Have a question? Open a Discussion in the Community tab above.

	If these quantizations were useful, please consider:
	- ⭐ Starring [quant-kit](https://github.com/DhruvalPtl/quant-kit) on GitHub
	- 👍 Liking this model on HuggingFace
	- 💬 Leaving feedback in the Community tab