Instructions to use digitranslab/Megamind-v3-4B-base-instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use digitranslab/Megamind-v3-4B-base-instruct-gguf with Transformers:

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

pipe = pipeline("text-generation", model="digitranslab/Megamind-v3-4B-base-instruct-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("digitranslab/Megamind-v3-4B-base-instruct-gguf", dtype="auto")

llama-cpp-python

How to use digitranslab/Megamind-v3-4B-base-instruct-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="digitranslab/Megamind-v3-4B-base-instruct-gguf",
	filename="Megamind-v3-4b-base-instruct-Q3_K_L.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 digitranslab/Megamind-v3-4B-base-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 digitranslab/Megamind-v3-4B-base-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf digitranslab/Megamind-v3-4B-base-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 digitranslab/Megamind-v3-4B-base-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf digitranslab/Megamind-v3-4B-base-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 digitranslab/Megamind-v3-4B-base-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf digitranslab/Megamind-v3-4B-base-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 digitranslab/Megamind-v3-4B-base-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf digitranslab/Megamind-v3-4B-base-instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/digitranslab/Megamind-v3-4B-base-instruct-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use digitranslab/Megamind-v3-4B-base-instruct-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "digitranslab/Megamind-v3-4B-base-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": "digitranslab/Megamind-v3-4B-base-instruct-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/digitranslab/Megamind-v3-4B-base-instruct-gguf:Q4_K_M

SGLang

How to use digitranslab/Megamind-v3-4B-base-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 "digitranslab/Megamind-v3-4B-base-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": "digitranslab/Megamind-v3-4B-base-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 "digitranslab/Megamind-v3-4B-base-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": "digitranslab/Megamind-v3-4B-base-instruct-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use digitranslab/Megamind-v3-4B-base-instruct-gguf with Ollama:
```
ollama run hf.co/digitranslab/Megamind-v3-4B-base-instruct-gguf:Q4_K_M
```

Unsloth Studio

How to use digitranslab/Megamind-v3-4B-base-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 digitranslab/Megamind-v3-4B-base-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 digitranslab/Megamind-v3-4B-base-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 digitranslab/Megamind-v3-4B-base-instruct-gguf to start chatting

How to use digitranslab/Megamind-v3-4B-base-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 digitranslab/Megamind-v3-4B-base-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": "digitranslab/Megamind-v3-4B-base-instruct-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use digitranslab/Megamind-v3-4B-base-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 digitranslab/Megamind-v3-4B-base-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 digitranslab/Megamind-v3-4B-base-instruct-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use digitranslab/Megamind-v3-4B-base-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 digitranslab/Megamind-v3-4B-base-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 "digitranslab/Megamind-v3-4B-base-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 digitranslab/Megamind-v3-4B-base-instruct-gguf with Docker Model Runner:
```
docker model run hf.co/digitranslab/Megamind-v3-4B-base-instruct-gguf:Q4_K_M
```

Lemonade

How to use digitranslab/Megamind-v3-4B-base-instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull digitranslab/Megamind-v3-4B-base-instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Megamind-v3-4B-base-instruct-gguf-Q4_K_M

List all available models

lemonade list

Megamind-v3-4B-base-instruct: a 4B baseline model for fine-tuning

Overview

Megamind-v3-4B-base-instruct is a 4B-parameter model obtained via post-training distillation from a larger teacher, transferring capabilities while preserving general-purpose performance on standard benchmarks. The result is a compact, ownable base that is straightforward to fine-tune, broadly applicable and minimizing the usual capacity–capability trade-offs.

Building on this base, Megamind-Code, a code-tuned variant, will be released soon.

Model Overview

This repo contains the BF16 version of Megamind-v3-4B-base-instruct, which has the following features:

Type: Causal Language Models
Training Stage: Pretraining & Post-training
Number of Parameters: 4B in total
Number of Layers: 36
Number of Attention Heads (GQA): 32 for Q and 8 for KV
Context Length: 262,144 natively.

Intended Use

A better small base for downstream work: improved instruction following out of the box, strong starting point for fine-tuning, and effective lightweight coding assistance.

Performance

Quick Start

Integration with Megaminds

Megamind-v3 demo is hosted on Megamind at chat.megamind.ai. It is also optimized for direct integration with Megamind, select the model in the app to start using it.

Local Deployment

Using vLLM:

vllm serve digitranslab/Megamind-v3-4B-base-instruct \
    --host 0.0.0.0 \
    --port 1234 \
    --enable-auto-tool-choice \
    --tool-call-parser hermes

Using llama.cpp:

llama-server --model Megamind-v3-4B-base-instruct-Q8_0.gguf \
    --host 0.0.0.0 \
    --port 1234 \
    --jinja \
    --no-context-shift

Recommended Parameters

For optimal performance in agentic and general tasks, we recommend the following inference parameters:

temperature: 0.7
top_p: 0.8
top_k: 20

🤝 Community & Support

Discussions: Hugging Face Community
Megamind: Learn more about the Megamind at megamind.ai

📄 Citation

Updated Soon

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GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

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