Instructions to use evalengine/unbound-e2b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use evalengine/unbound-e2b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="evalengine/unbound-e2b-gguf",
	filename="mmproj-unbound-e2b.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use evalengine/unbound-e2b-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf evalengine/unbound-e2b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf evalengine/unbound-e2b-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 evalengine/unbound-e2b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf evalengine/unbound-e2b-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 evalengine/unbound-e2b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf evalengine/unbound-e2b-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 evalengine/unbound-e2b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf evalengine/unbound-e2b-gguf:Q4_K_M

Use Docker

docker model run hf.co/evalengine/unbound-e2b-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use evalengine/unbound-e2b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "evalengine/unbound-e2b-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": "evalengine/unbound-e2b-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/evalengine/unbound-e2b-gguf:Q4_K_M

Ollama
How to use evalengine/unbound-e2b-gguf with Ollama:
```
ollama run hf.co/evalengine/unbound-e2b-gguf:Q4_K_M
```

Unsloth Studio new

How to use evalengine/unbound-e2b-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 evalengine/unbound-e2b-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 evalengine/unbound-e2b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for evalengine/unbound-e2b-gguf to start chatting

Pi new

How to use evalengine/unbound-e2b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf evalengine/unbound-e2b-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": "evalengine/unbound-e2b-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use evalengine/unbound-e2b-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 evalengine/unbound-e2b-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 evalengine/unbound-e2b-gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use evalengine/unbound-e2b-gguf with Docker Model Runner:
```
docker model run hf.co/evalengine/unbound-e2b-gguf:Q4_K_M
```

Lemonade

How to use evalengine/unbound-e2b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull evalengine/unbound-e2b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.unbound-e2b-gguf-Q4_K_M

List all available models

lemonade list

unbound-e2b-gguf / README.md

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	---
	license: apache-2.0
	base_model: evalengine/unbound-e2b
	base_model_relation: quantized
	tags:
	- gguf
	- gemma4
	- gemma
	- gemma-4
	- uncensored
	- on-device
	pipeline_tag: image-text-to-text
	---

	<p align="center">
	<img src="unbound-logo.svg" alt="Unbound" width="160" height="160">
	</p>

	# Unbound E2B GGUF — because there is no boundary

	> No guarantee — use at your own risk. Reduced safety filtering; can
	> produce harmful or false output. Provided as-is.

	GGUF quants of [`evalengine/unbound-e2b`](https://huggingface.co/evalengine/unbound-e2b)
	for Ollama, llama.cpp, LM Studio, and [wllama](https://github.com/ngxson/wllama)
	(in-browser). Built by [Chromia](https://x.com/Chromia) and
	[Eval Engine](https://x.com/eval_engine).

	## Available quants

	Each quant is shipped as a sharded multi-part GGUF (`unbound-e2b.<QUANT>-NNNNN-of-NNNNN.gguf`).
	Ollama, llama.cpp, LM Studio, and wllama auto-stitch on the first part —
	same UX as a single file.

	\| Quant \| Parts \| Total \| Browser (wllama) \| Desktop \| Notes \|
	\|---------\|-------\|--------\|------------------\|---------\|-------\|
	\| Q2_K \| 3 \| 2.8 GB \| ✅ \| ✅ \| Smallest, biggest quality drop \|
	\| Q3_K_M \| 3 \| 3.0 GB \| ✅ \| ✅ \| Marginal size win over Q4 \|
	\| Q4_K_M \| 3 \| 3.2 GB \| ✅ \| ✅ \| Recommended default \|
	\| Q6_K \| 4 \| 3.6 GB \| ✅ \| ✅ \| Higher fidelity \|
	\| Q8_0 \| 4 \| 4.6 GB \| ❌ (over 2 GB) \| ✅ \| Highest fidelity; desktop only \|

	`mmproj-unbound-e2b.gguf` (vision projector, ~942 MB) sits at the repo
	root — load it alongside any LM quant for image input. See Vision below.

	## Sampling

	- Creative / open-ended → `temperature=1.0, top_p=0.95, top_k=64`.
	- Factual / brand questions → drop `temperature` to ~0.3–0.5.
	- llama.cpp: pass `--jinja`. Gemma 4 thinking mode is on by default; set
	`enable_thinking: false` in chat-template kwargs for shorter replies.

	For Ollama, pull from the Ollama Registry —
	`ollama pull hf.co/...` [doesn't yet support sharded GGUFs](https://github.com/ollama/ollama/issues/5245).
	The registry version is a single-file Q4_K_M with a bundled Modelfile
	(`temperature=0.6, top_p=0.95, top_k=64, repeat_penalty=1.05, num_ctx=8192`
	and an identity-grounding system prompt).

	## Run

	```bash
	# Ollama Registry (single-file Q4_K_M, identity-grounded Modelfile)
	ollama pull evalengine/unbound-e2b
	ollama run evalengine/unbound-e2b
	```

	```bash
	# llama.cpp — point at FIRST shard, the rest auto-stitch
	./llama-cli -m unbound-e2b.Q4_K_M-00001-of-00003.gguf -p "your prompt"
	```

	```js
	// wllama (browser) — Q8_0 has a tensor over 2 GB; use Q2/Q3/Q4/Q6
	import { Wllama } from '@wllama/wllama';
	const wllama = new Wllama(/* … */);
	await wllama.loadModelFromHF(
	'evalengine/unbound-e2b-GGUF',
	'unbound-e2b.Q4_K_M-00001-of-00003.gguf'
	);
	```

	## Vision / image input (optional)

	`mmproj-unbound-e2b.gguf` enables image-to-text. Pair with any LM quant via
	`llama-mtmd-cli` or `llama-gemma3-cli`:

	```bash
	./llama-mtmd-cli \
	-m unbound-e2b.Q4_K_M-00001-of-00003.gguf \
	--mmproj mmproj-unbound-e2b.gguf \
	--image path/to/your/image.png \
	-p "What is in this image?"
	```

	> Disclaimer. The vision encoder is **Google's original weights,
	> unchanged** — abliteration only touched the language model. The LM is
	> uncensored, but the vision encoder may still suppress features for
	> content classes Google's base was tuned against. We have **not
	> benchmarked the visual axis**. Treat as preview.

	Text-only: skip `--mmproj` entirely. Standard `llama-cli` / Ollama / LM
	Studio do not need the mmproj file.

	## Acknowledgements

	Fine-tuned with [Unsloth](https://github.com/unslothai/unsloth) + HF
	[TRL](https://github.com/huggingface/trl). Abliteration via
	[heretic](https://github.com/p-e-w/heretic). Environment from
	[autoresearch](https://github.com/karpathy/autoresearch). Compliance training data distilled from the [AEON](https://huggingface.co/AEON-7) uncensored teacher model.

	## Links

	- Unbound — [unbound.evalengine.ai](https://unbound.evalengine.ai)
	- Eval Engine — [evalengine.ai](https://evalengine.ai) · [X / Twitter](https://x.com/eval_engine)
	- Token — [CoinGecko](https://www.coingecko.com/en/coins/chromia-s-eval-by-virtuals) · [CoinMarketCap](https://coinmarketcap.com/currencies/eval-engine/)

	## License

	Apache-2.0, inherited from `google/gemma-4-E2B-it`. Full model card +
	benchmarks at [`evalengine/unbound-e2b`](https://huggingface.co/evalengine/unbound-e2b).