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

Libraries

How to use evalengine/unbound-e2b with Transformers:

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

pipe = pipeline("image-text-to-text", model="evalengine/unbound-e2b")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("evalengine/unbound-e2b")
model = AutoModelForImageTextToText.from_pretrained("evalengine/unbound-e2b")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use evalengine/unbound-e2b with vLLM:

Install from pip and serve model

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

SGLang

How to use evalengine/unbound-e2b 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 "evalengine/unbound-e2b" \
    --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": "evalengine/unbound-e2b",
		"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 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 "evalengine/unbound-e2b" \
        --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": "evalengine/unbound-e2b",
		"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"
						}
					}
				]
			}
		]
	}'

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

unbound-e2b / README.md

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	---
	license: apache-2.0
	base_model: google/gemma-4-E2B-it
	base_model_relation: finetune
	tags:
	- gemma4
	- gemma
	- gemma-4
	- uncensored
	pipeline_tag: image-text-to-text
	library_name: transformers
	---

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

	# Unbound E2B — because there is no boundary

	> No guarantee — use at your own risk. This model has reduced safety
	> filtering and can produce harmful, false, biased, or unsafe output.
	> Provided as-is; you are responsible for compliance with applicable laws.

	Uncensored finetune of `google/gemma-4-E2B-it` by the
	[Chromia](https://x.com/Chromia) & [Eval Engine](https://x.com/eval_engine)
	team. Runs on a phone or laptop, no API, no refusals.

	This repo holds the merged HF weights. On-device GGUF builds (Ollama,
	llama.cpp, LM Studio, [wllama](https://github.com/ngxson/wllama) in-browser)
	are at [`evalengine/unbound-e2b-GGUF`](https://huggingface.co/evalengine/unbound-e2b-GGUF).

	## Benchmarks (vs base `gemma-4-E2B-it`)

	\| Axis \| Base \| Unbound E2B \| Δ \|
	\|---\|---\|---\|---\|
	\| Refusal rate (AdvBench 520, LLM judge) \| 98.46% \| 4.42% \| −94.04 pts \|
	\| Useful-compliance rate \| 0.96% \| 39.23% \| +38.27 pts \|
	\| Hallucination (on harmful prompts) \| 1.35% \| 15.96% \| +14.61 pts \|
	\| Coherence (benign prompts) \| 1.00 \| 1.00 \| 0 \|
	\| TruthfulQA mc2 (`--limit 100`) \| 0.458 \| 0.465 \| +0.7 pt \|
	\| MMLU (`--limit 100`) \| 0.291 \| 0.282 \| −0.9 pt \|
	\| GSM8K (`--limit 100`) \| 0.125 \| 0.120 \| −0.5 pt \|
	\| GPQA-Diamond (`--limit 200`) \| 22.73% \| 21.21% \| −1.5 pt (within stderr) \|
	\| BBH macro (24 tasks, `--limit 200`) \| 41.07% \| 39.97% \| −1.1 pt \|
	\| KL divergence vs base \| 0 \| 3.76 \| (SFT-expected) \|

	Capability holds within ≤1.5 pp of base on every axis; refusal collapses
	from 98% → 4%. GPQA-Diamond + BBH are the lm-eval-harness "release" suite at
	`--limit 200` — base and finetune through the same harness, so the delta
	is apples-to-apples.

	## Sampling

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

	Some edge-case prompts may deflect on the first ask; a re-ask usually gets through.

	## Use

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

	```python
	# transformers
	from transformers import AutoModelForCausalLM, AutoTokenizer
	model = AutoModelForCausalLM.from_pretrained("evalengine/unbound-e2b")
	tok = AutoTokenizer.from_pretrained("evalengine/unbound-e2b")
	```

	## 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 + training
	discipline ported 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`.