Instructions to use Nexuss0781/SPACE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Nexuss0781/SPACE 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 Nexuss0781/SPACE:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nexuss0781/SPACE:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Nexuss0781/SPACE:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nexuss0781/SPACE: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 Nexuss0781/SPACE:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Nexuss0781/SPACE: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 Nexuss0781/SPACE:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nexuss0781/SPACE:Q4_K_M

Use Docker

docker model run hf.co/Nexuss0781/SPACE:Q4_K_M

LM Studio
Jan

vLLM

How to use Nexuss0781/SPACE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nexuss0781/SPACE"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nexuss0781/SPACE",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Nexuss0781/SPACE:Q4_K_M

Ollama
How to use Nexuss0781/SPACE with Ollama:
```
ollama run hf.co/Nexuss0781/SPACE:Q4_K_M
```
Unsloth Desktop

How to use Nexuss0781/SPACE with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nexuss0781/SPACE: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": "Nexuss0781/SPACE:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Nexuss0781/SPACE with Docker Model Runner:
```
docker model run hf.co/Nexuss0781/SPACE:Q4_K_M
```

Lemonade

How to use Nexuss0781/SPACE with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nexuss0781/SPACE:Q4_K_M

Run and chat with the model

lemonade run user.SPACE-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Nexuss0781/SPACE with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nexuss0781/SPACE: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 Nexuss0781/SPACE:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Nexuss0781/SPACE with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nexuss0781/SPACE: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 "Nexuss0781/SPACE: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"

SPACE / README.md

Nexuss0781

Add model card (neutral wording)

a811bf4 verified about 2 months ago

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---
license: other
pipeline_tag: image-text-to-text
tags:
  - text-generation
  - image-text-to-text
quantized_by: Nexuss0781
---
# SPACE

## Text (10 prompts, 256 max new tokens)

| Model | Weights | Avg tok/s | Peak RSS | Wall (10 prompts) |
|---|---:|---:|---:|---:|
| **SmolLM2-360M-Instruct** (q4_k_m) | 0.27 GB | **57.73** | 474 MB | 37.7s |
| SmolLM3-3B (q4_k_m) | 1.92 GB | 13.18 | 3,436 MB | 196.6s |
| Qwen2.5-3B-Instruct (q4_k_m) | 1.93 GB | 13.43 | 3,336 MB | 161.6s |

## Vision (10 image tasks, 128 max new tokens)

| Model | Weights | Avg tok/s | Peak RSS | Wall (10 imgs) |
|---|---:|---:|---:|---:|
| **SmolVLM2-500M-Video-Instruct** (q8_0) | 0.44 GB | **67.15** | 1,026 MB | 73.7s |
| SmolVLM-500M-Instruct (q8_0) | 0.44 GB | 65.06 | 1,026 MB | 77.6s |
| SmolVLM2-2.2B-Instruct (q4_k_m) | 1.11 GB | 19.15 | 4,260 MB | 363.3s |
| Moondream2 (q8_0) | 1.51 GB | 14.86 | 4,000 MB | 236.1s |
| Gemma 3 4B IT (q4_k_m) | 2.49 GB | 13.19 | 5,309 MB | 602.6s |
## Audio (10 speech clips, 128 max new tokens)
| Model | Weights | Avg tok/s | Peak RSS | Wall (10 clips) |
|---|---|---:|---:|---:|
| **Qwen3-ASR-0.6B** (q8_0) | 0.80 GB | **37.17** | 2,073 MB | 24.1s |
| Ultravox 1B (q4_k_m) | 0.81 GB | 35.68 | 2,969 MB | 209.2s |
| Qwen2.5-Omni-3B (q4_k_m) | 2.10 GB | 15.90 | 6,302 MB | 435.4s |
| Qwen3-ASR-1.7B (q8_0) | 2.17 GB | 12.68 | 3,510 MB | 45.6s |
| Ultravox 8B (q4_k_m) | 4.92 GB | 7.34 | 10,456 MB | 448.1s |
## Selected
| Modality | Micro (always-on) | Quality |
|---|---|---|
| Text | **SmolLM2-360M-Instruct** (q4_k_m, 57.7 tok/s) | **Qwen2.5-3B-Instruct** (q4_k_m, 13.4 tok/s) |
| Vision | **SmolVLM2-500M-Video-Instruct** (q8_0, 67.2 tok/s) | **SmolVLM2-2.2B-Instruct** (q4_k_m, 19.2 tok/s) |
| Audio | **Qwen3-ASR-0.6B** (q8_0, 37.2 tok/s) | **Qwen3-ASR-1.7B** (q8_0, 12.7 tok/s) |

	---
	license: other
	pipeline_tag: image-text-to-text
	tags:
	- text-generation
	- image-text-to-text
	quantized_by: Nexuss0781
	---

	# SPACE

	## Text (10 prompts, 256 max new tokens)

	\| Model \| Weights \| Avg tok/s \| Peak RSS \| Wall (10 prompts) \|
	\|---\|---:\|---:\|---:\|---:\|
	\| SmolLM2-360M-Instruct (q4_k_m) \| 0.27 GB \| 57.73 \| 474 MB \| 37.7s \|
	\| SmolLM3-3B (q4_k_m) \| 1.92 GB \| 13.18 \| 3,436 MB \| 196.6s \|
	\| Qwen2.5-3B-Instruct (q4_k_m) \| 1.93 GB \| 13.43 \| 3,336 MB \| 161.6s \|

	## Vision (10 image tasks, 128 max new tokens)

	\| Model \| Weights \| Avg tok/s \| Peak RSS \| Wall (10 imgs) \|
	\|---\|---:\|---:\|---:\|---:\|
	\| SmolVLM2-500M-Video-Instruct (q8_0) \| 0.44 GB \| 67.15 \| 1,026 MB \| 73.7s \|
	\| SmolVLM-500M-Instruct (q8_0) \| 0.44 GB \| 65.06 \| 1,026 MB \| 77.6s \|
	\| SmolVLM2-2.2B-Instruct (q4_k_m) \| 1.11 GB \| 19.15 \| 4,260 MB \| 363.3s \|
	\| Moondream2 (q8_0) \| 1.51 GB \| 14.86 \| 4,000 MB \| 236.1s \|
	\| Gemma 3 4B IT (q4_k_m) \| 2.49 GB \| 13.19 \| 5,309 MB \| 602.6s \|

	## Audio (10 speech clips, 128 max new tokens)

	\| Model \| Weights \| Avg tok/s \| Peak RSS \| Wall (10 clips) \|
	\|---\|---\|---:\|---:\|---:\|
	\| Qwen3-ASR-0.6B (q8_0) \| 0.80 GB \| 37.17 \| 2,073 MB \| 24.1s \|
	\| Ultravox 1B (q4_k_m) \| 0.81 GB \| 35.68 \| 2,969 MB \| 209.2s \|
	\| Qwen2.5-Omni-3B (q4_k_m) \| 2.10 GB \| 15.90 \| 6,302 MB \| 435.4s \|
	\| Qwen3-ASR-1.7B (q8_0) \| 2.17 GB \| 12.68 \| 3,510 MB \| 45.6s \|
	\| Ultravox 8B (q4_k_m) \| 4.92 GB \| 7.34 \| 10,456 MB \| 448.1s \|

	## Selected

	\| Modality \| Micro (always-on) \| Quality \|
	\|---\|---\|---\|
	\| Text \| SmolLM2-360M-Instruct (q4_k_m, 57.7 tok/s) \| Qwen2.5-3B-Instruct (q4_k_m, 13.4 tok/s) \|
	\| Vision \| SmolVLM2-500M-Video-Instruct (q8_0, 67.2 tok/s) \| SmolVLM2-2.2B-Instruct (q4_k_m, 19.2 tok/s) \|
	\| Audio \| Qwen3-ASR-0.6B (q8_0, 37.2 tok/s) \| Qwen3-ASR-1.7B (q8_0, 12.7 tok/s) \|