Instructions to use ram1234598766/Cesium2-vision-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ram1234598766/Cesium2-vision-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 ram1234598766/Cesium2-vision-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf ram1234598766/Cesium2-vision-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ram1234598766/Cesium2-vision-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf ram1234598766/Cesium2-vision-GGUF:F16

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 ram1234598766/Cesium2-vision-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf ram1234598766/Cesium2-vision-GGUF:F16

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 ram1234598766/Cesium2-vision-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ram1234598766/Cesium2-vision-GGUF:F16

Use Docker

docker model run hf.co/ram1234598766/Cesium2-vision-GGUF:F16

LM Studio
Jan

vLLM

How to use ram1234598766/Cesium2-vision-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ram1234598766/Cesium2-vision-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": "ram1234598766/Cesium2-vision-GGUF",
		"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/ram1234598766/Cesium2-vision-GGUF:F16

Ollama
How to use ram1234598766/Cesium2-vision-GGUF with Ollama:
```
ollama run hf.co/ram1234598766/Cesium2-vision-GGUF:F16
```

Unsloth Studio

How to use ram1234598766/Cesium2-vision-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 ram1234598766/Cesium2-vision-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 ram1234598766/Cesium2-vision-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ram1234598766/Cesium2-vision-GGUF to start chatting

Docker Model Runner
How to use ram1234598766/Cesium2-vision-GGUF with Docker Model Runner:
```
docker model run hf.co/ram1234598766/Cesium2-vision-GGUF:F16
```

Lemonade

How to use ram1234598766/Cesium2-vision-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ram1234598766/Cesium2-vision-GGUF:F16

Run and chat with the model

lemonade run user.Cesium2-vision-GGUF-F16

List all available models

lemonade list

Atomic Chat

Cesium2 Vision — GGUF (Q4_K_M + mmproj)

MORPH-AI v7 (Cesium2) Vision — a QLoRA fine-tune of Qwen2.5-VL-3B-Instruct (document VQA from The Cauldron + the full Cesium2 2026-knowledge/persona text set), merged and exported for llama.cpp/Ollama.

Files

File	Role	Size
`cesium2-vision-q4_k_m.gguf`	LLM tower (fine-tuned)	1.93 GB
`cesium2-vision-mmproj-f16.gguf`	CLIP vision projector	1.34 GB

Both files are required for image input.

Quick start (Ollama)

ollama pull ram1234598766/cesium2-vision

Manual import: put both files in one folder with a Modelfile:

FROM ./cesium2-vision-q4_k_m.gguf
FROM ./cesium2-vision-mmproj-f16.gguf
PARAMETER num_ctx 8192
PARAMETER stop "<|im_end|>"

Model facts


Base	Qwen/Qwen2.5-VL-3B-Instruct
Fine-tune	QLoRA r=16 on attention projections, 1 epoch
Data	400 docvqa images + Cesium2 2026 persona/knowledge set
Architecture	qwen2vl + clip projector

⚡ What's new (August 2026)

🌐 Web-connected Playground — toggle Live web search (keyless DuckDuckGo) and URL reading/scraping: grounded answers with cited sources
💾 Sessions stored per account — full sessions API (GET/POST /v1/sessions), chat history follows you across devices
🔑 Two key types — cs2-pg-… Playground keys (browser chat) and cs2-sk-… Agent keys (Claude Code, OpenAI SDK, curl), each with its own usage analytics
🧰 Tools API — POST /v1/tools/search (live web), POST /v1/tools/read (scrape), plus Wikipedia, GitHub, Hugging Face, arXiv, exact-math and clock connectors via POST /v1/tools/use
⌨️ VS Code extension 1.1.0 — inline completions, web-grounded chat, cloud sessions, 14 coding commands, slick animations
⚡ Faster serving — models pre-warmed and pinned in RAM, consistent 8k context, 60s search cache, 8-way parallel
🌐 Live API & key portal: https://cesium2.tail4baa64.ts.net (sign in with Google → free personal key, works with Claude Code)
💻 Source & pipeline: https://github.com/ram1234598766-dotcom/cesium2
📝 Text sibling model: https://huggingface.co/ram1234598766/Cesium2-v7-GGUF
🧩 VS Code assistant with drag-&-drop vision: https://open-vsx.org/extension/ram1234598766/cesium2-ai
🫥 Ollama page: https://ollama.com/ram1234598766/cesium2-vision

Author: Mrityunjay K — trained, fine-tuned, quantized and shipped end-to-end on free Kaggle GPUs. Live API: https://cesium2.tail4baa64.ts.net

Downloads last month: -

GGUF

Model size

3B params

Architecture

qwen2vl

Hardware compatibility

4-bit

ram1234598766
/

Cesium2-vision-GGUF

Cesium2 Vision — GGUF (Q4_K_M + mmproj)

Files

Quick start (Ollama)

Model facts

Links

⚡ What's new (August 2026)