Instructions to use second-state/Megrez-3B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use second-state/Megrez-3B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="second-state/Megrez-3B-Instruct-GGUF",
	filename="Megrez-3B-Instruct-Q2_K.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 second-state/Megrez-3B-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 second-state/Megrez-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/Megrez-3B-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 second-state/Megrez-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/Megrez-3B-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 second-state/Megrez-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/Megrez-3B-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 second-state/Megrez-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/Megrez-3B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/second-state/Megrez-3B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use second-state/Megrez-3B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "second-state/Megrez-3B-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": "second-state/Megrez-3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/second-state/Megrez-3B-Instruct-GGUF:Q4_K_M

Ollama
How to use second-state/Megrez-3B-Instruct-GGUF with Ollama:
```
ollama run hf.co/second-state/Megrez-3B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use second-state/Megrez-3B-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 second-state/Megrez-3B-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 second-state/Megrez-3B-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 second-state/Megrez-3B-Instruct-GGUF to start chatting

Docker Model Runner
How to use second-state/Megrez-3B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/Megrez-3B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use second-state/Megrez-3B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/Megrez-3B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Megrez-3B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Megrez-3B-Instruct-GGUF

Original Model

Infinigence/Megrez-3B-Instruct

Run with LlamaEdge

LlamaEdge version: v0.16.0

Prompt template

Prompt type: megrez

Prompt string

<|role_start|>system<|role_end|>{system_message}<|turn_end|><|role_start|>user<|role_end|>{user_message}<|turn_end|><|role_start|>assistant<|role_end|>

Context size: 32000
Run as LlamaEdge service
```
wasmedge --dir .:. --nn-preload default:GGML:AUTO:Megrez-3B-Instruct-Q5_K_M.gguf \
  llama-api-server.wasm \
  --model-name Megrez-3B-Instruct \
  --prompt-template megrez \
  --ctx-size 32000
```
For use cases of conversations or article writing, temperature=0.7 is strongly recommended. For use cases of mathematics or logical reasoning, temperature=0.2 is strongly recommended.
Run as LlamaEdge command app
```
wasmedge --dir .:. --nn-preload default:GGML:AUTO:Megrez-3B-Instruct-Q5_K_M.gguf \
  llama-chat.wasm \
  --prompt-template megrez \
  --ctx-size 32000
```
For use cases of conversations or article writing, temperature=0.7 is strongly recommended. For use cases of mathematics or logical reasoning, temperature=0.2 is strongly recommended.

Quantized GGUF Models

Name	Quant method	Bits	Size	Use case
Megrez-3B-Instruct-Q2_K.gguf	Q2_K	2	1.21 GB	smallest, significant quality loss - not recommended for most purposes
Megrez-3B-Instruct-Q3_K_L.gguf	Q3_K_L	3	1.60 GB	small, substantial quality loss
Megrez-3B-Instruct-Q3_K_M.gguf	Q3_K_M	3	1.50 GB	very small, high quality loss
Megrez-3B-Instruct-Q3_K_S.gguf	Q3_K_S	3	1.38 GB	very small, high quality loss
Megrez-3B-Instruct-Q4_0.gguf	Q4_0	4	1.73 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Megrez-3B-Instruct-Q4_K_M.gguf	Q4_K_M	4	1.81 GB	medium, balanced quality - recommended
Megrez-3B-Instruct-Q4_K_S.gguf	Q4_K_S	4	1.74 GB	small, greater quality loss
Megrez-3B-Instruct-Q5_0.gguf	Q5_0	5	2.05 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Megrez-3B-Instruct-Q5_K_M.gguf	Q5_K_M	5	2.09 GB	large, very low quality loss - recommended
Megrez-3B-Instruct-Q5_K_S.gguf	Q5_K_S	5	2.05 GB	large, low quality loss - recommended
Megrez-3B-Instruct-Q6_K.gguf	Q6_K	6	2.40 GB	very large, extremely low quality loss
Megrez-3B-Instruct-Q8_0.gguf	Q8_0	8	3.10 GB	very large, extremely low quality loss - not recommended
Megrez-3B-Instruct-f16.gguf	f16	16	5.84 GB

Quantized with llama.cpp b4381

Downloads last month: 118

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for second-state/Megrez-3B-Instruct-GGUF

Base model

Infinigence/Megrez-3B-Instruct

Quantized

(6)

this model