Instructions to use evalengine/unbound-e4b-wllama-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use evalengine/unbound-e4b-wllama-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="evalengine/unbound-e4b-wllama-gguf",
	filename="mmproj-unbound-e4b.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-e4b-wllama-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-e4b-wllama-gguf:Q2_K
# Run inference directly in the terminal:
llama-cli -hf evalengine/unbound-e4b-wllama-gguf:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf evalengine/unbound-e4b-wllama-gguf:Q2_K
# Run inference directly in the terminal:
llama-cli -hf evalengine/unbound-e4b-wllama-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 evalengine/unbound-e4b-wllama-gguf:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf evalengine/unbound-e4b-wllama-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 evalengine/unbound-e4b-wllama-gguf:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf evalengine/unbound-e4b-wllama-gguf:Q2_K

Use Docker

docker model run hf.co/evalengine/unbound-e4b-wllama-gguf:Q2_K

LM Studio
Jan

vLLM

How to use evalengine/unbound-e4b-wllama-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "evalengine/unbound-e4b-wllama-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-e4b-wllama-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-e4b-wllama-gguf:Q2_K

Ollama
How to use evalengine/unbound-e4b-wllama-gguf with Ollama:
```
ollama run hf.co/evalengine/unbound-e4b-wllama-gguf:Q2_K
```

Unsloth Studio new

How to use evalengine/unbound-e4b-wllama-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-e4b-wllama-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-e4b-wllama-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-e4b-wllama-gguf to start chatting

Pi new

How to use evalengine/unbound-e4b-wllama-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-e4b-wllama-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": "evalengine/unbound-e4b-wllama-gguf:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use evalengine/unbound-e4b-wllama-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-e4b-wllama-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 evalengine/unbound-e4b-wllama-gguf:Q2_K

Run Hermes

hermes

Docker Model Runner
How to use evalengine/unbound-e4b-wllama-gguf with Docker Model Runner:
```
docker model run hf.co/evalengine/unbound-e4b-wllama-gguf:Q2_K
```

Lemonade

How to use evalengine/unbound-e4b-wllama-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull evalengine/unbound-e4b-wllama-gguf:Q2_K

Run and chat with the model

lemonade run user.unbound-e4b-wllama-gguf-Q2_K

List all available models

lemonade list

unbound-e4b-wllama-gguf / README.md

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metadata

license: apache-2.0
base_model: evalengine/unbound-e4b
base_model_relation: quantized
tags:
  - gguf
  - gemma4
  - gemma
  - gemma-4
  - uncensored
  - on-device
  - wllama
  - browser
pipeline_tag: image-text-to-text

Unbound E4B (wllama / browser builds) — because there is no boundary

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

Browser-safe GGUF quants of evalengine/unbound-e4b for wllama. Built by Chromia and Eval Engine.

Desktop / Ollama / llama.cpp / LM Studio users: use evalengine/unbound-e4b-GGUF instead — the desktop builds are faster and don't pay the embedding-precision compromise these browser-safe builds make.

Why a separate repo?

E4B's per_layer_token_embd is a 2.82-billion-value tensor. At llama.cpp's default Q6_K precision it lands at ~~2.2 GB — over wllama's 2 GB ArrayBuffer cap. These variants force embeddings to q5_K (~~1.85 GB) so the largest part fits in the browser. Layer weights are unchanged from the matching desktop quant.

A dedicated repo with the unbound-e4b-wllama model prefix prevents HF's GGUF UI from aggregating these with the same-quant desktop files (unbound-e4b.Q4_K_M-... vs unbound-e4b-wllama.Q4_K_M-...).

Available quants

Each quant is shipped as a sharded multi-part GGUF (unbound-e4b-wllama.<QUANT>-NNNNN-of-NNNNN.gguf). wllama auto-stitches on the first part.

Variant	Parts	Total	Notes
Q4_K_M	4	4.51 GB	Recommended — layers @ Q4_K_M, embed @ q5_K
Q2_K	4	3.69 GB	Smallest browser-loadable — layers @ Q2_K, embed @ q5_K

Run

// wllama (browser)
import { Wllama } from '@wllama/wllama';
const wllama = new Wllama(/* … */);
await wllama.loadModelFromHF(
  'evalengine/unbound-e4b-wllama-gguf',
  'unbound-e4b-wllama.Q4_K_M-00001-of-00004.gguf'
);

Sampling

Creative / open-ended → temperature=1.0, top_p=0.95, top_k=64.
Factual / brand questions → drop temperature to ~0.3–0.5.

Vision / image input (optional)

mmproj-unbound-e4b.gguf (vision projector, ~942 MB) is also in this repo so browser users don't bounce between repos. Pair with any quant via your wllama-compatible vision pipeline.

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.

Acknowledgements

Fine-tuned with Unsloth + HF TRL. Abliteration via heretic. Environment from autoresearch. Compliance training data distilled from the AEON uncensored teacher model.

License

Apache-2.0, inherited from google/gemma-4-E4B-it. Full model card + benchmarks at evalengine/unbound-e4b.

evalengine
/

unbound-e4b-wllama-gguf