Instructions to use tensorblock/Multiverse4FM_Multiverse-32B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/Multiverse4FM_Multiverse-32B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="tensorblock/Multiverse4FM_Multiverse-32B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/Multiverse4FM_Multiverse-32B-GGUF", dtype="auto")

llama-cpp-python

How to use tensorblock/Multiverse4FM_Multiverse-32B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/Multiverse4FM_Multiverse-32B-GGUF",
	filename="Multiverse-32B-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 tensorblock/Multiverse4FM_Multiverse-32B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/Multiverse4FM_Multiverse-32B-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/Multiverse4FM_Multiverse-32B-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/Multiverse4FM_Multiverse-32B-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/Multiverse4FM_Multiverse-32B-GGUF:Q2_K

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 tensorblock/Multiverse4FM_Multiverse-32B-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/Multiverse4FM_Multiverse-32B-GGUF:Q2_K

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 tensorblock/Multiverse4FM_Multiverse-32B-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/Multiverse4FM_Multiverse-32B-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/Multiverse4FM_Multiverse-32B-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use tensorblock/Multiverse4FM_Multiverse-32B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tensorblock/Multiverse4FM_Multiverse-32B-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": "tensorblock/Multiverse4FM_Multiverse-32B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tensorblock/Multiverse4FM_Multiverse-32B-GGUF:Q2_K

SGLang

How to use tensorblock/Multiverse4FM_Multiverse-32B-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 "tensorblock/Multiverse4FM_Multiverse-32B-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": "tensorblock/Multiverse4FM_Multiverse-32B-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 "tensorblock/Multiverse4FM_Multiverse-32B-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": "tensorblock/Multiverse4FM_Multiverse-32B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use tensorblock/Multiverse4FM_Multiverse-32B-GGUF with Ollama:
```
ollama run hf.co/tensorblock/Multiverse4FM_Multiverse-32B-GGUF:Q2_K
```

Unsloth Studio new

How to use tensorblock/Multiverse4FM_Multiverse-32B-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 tensorblock/Multiverse4FM_Multiverse-32B-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 tensorblock/Multiverse4FM_Multiverse-32B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tensorblock/Multiverse4FM_Multiverse-32B-GGUF to start chatting

Pi new

How to use tensorblock/Multiverse4FM_Multiverse-32B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf tensorblock/Multiverse4FM_Multiverse-32B-GGUF:Q2_K

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": "tensorblock/Multiverse4FM_Multiverse-32B-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use tensorblock/Multiverse4FM_Multiverse-32B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf tensorblock/Multiverse4FM_Multiverse-32B-GGUF:Q2_K

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 tensorblock/Multiverse4FM_Multiverse-32B-GGUF:Q2_K

Run Hermes

hermes

Docker Model Runner
How to use tensorblock/Multiverse4FM_Multiverse-32B-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/Multiverse4FM_Multiverse-32B-GGUF:Q2_K
```

Lemonade

How to use tensorblock/Multiverse4FM_Multiverse-32B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/Multiverse4FM_Multiverse-32B-GGUF:Q2_K

Run and chat with the model

lemonade run user.Multiverse4FM_Multiverse-32B-GGUF-Q2_K

List all available models

lemonade list

Multiverse4FM/Multiverse-32B - GGUF

Join our Discord to learn more about what we're building ↗

This repo contains GGUF format model files for Multiverse4FM/Multiverse-32B.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5753.

Our projects

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Prompt template

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Model file specification

Filename	Quant type	File Size	Description
Multiverse-32B-Q2_K.gguf	Q2_K	12.311 GB	smallest, significant quality loss - not recommended for most purposes
Multiverse-32B-Q3_K_S.gguf	Q3_K_S	14.390 GB	very small, high quality loss
Multiverse-32B-Q3_K_M.gguf	Q3_K_M	15.933 GB	very small, high quality loss
Multiverse-32B-Q3_K_L.gguf	Q3_K_L	17.245 GB	small, substantial quality loss
Multiverse-32B-Q4_0.gguf	Q4_0	18.638 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Multiverse-32B-Q4_K_S.gguf	Q4_K_S	18.782 GB	small, greater quality loss
Multiverse-32B-Q4_K_M.gguf	Q4_K_M	19.849 GB	medium, balanced quality - recommended
Multiverse-32B-Q5_0.gguf	Q5_0	22.635 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Multiverse-32B-Q5_K_S.gguf	Q5_K_S	22.635 GB	large, low quality loss - recommended
Multiverse-32B-Q5_K_M.gguf	Q5_K_M	23.259 GB	large, very low quality loss - recommended
Multiverse-32B-Q6_K.gguf	Q6_K	26.883 GB	very large, extremely low quality loss
Multiverse-32B-Q8_0.gguf	Q8_0	34.817 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/Multiverse4FM_Multiverse-32B-GGUF --include "Multiverse-32B-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/Multiverse4FM_Multiverse-32B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Downloads last month: 13

GGUF

Model size

33B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

Model tree for tensorblock/Multiverse4FM_Multiverse-32B-GGUF

Base model

Qwen/Qwen2.5-32B

Finetuned

Qwen/Qwen2.5-32B-Instruct

Finetuned

Multiverse4FM/Multiverse-32B

Quantized

(3)

this model

tensorblock
/

Multiverse4FM_Multiverse-32B-GGUF