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

Libraries

How to use evalengine/unbound-e4b with Transformers:

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

pipe = pipeline("image-text-to-text", model="evalengine/unbound-e4b")
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-e4b")
model = AutoModelForImageTextToText.from_pretrained("evalengine/unbound-e4b")
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-e4b with vLLM:

Install from pip and serve model

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

SGLang

How to use evalengine/unbound-e4b 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-e4b" \
    --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-e4b",
		"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-e4b" \
        --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-e4b",
		"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-e4b with Docker Model Runner:
```
docker model run hf.co/evalengine/unbound-e4b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Unbound E4B — 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-E4B-it by the Chromia & Eval Engine team — the larger sibling of evalengine/unbound-e2b. ~2× the parameters of E2B, noticeably stronger on knowledge + reasoning, still fits on a modern laptop.

This repo holds the merged HF weights. On-device GGUF builds (Ollama, llama.cpp, LM Studio, wllama in-browser) are at evalengine/unbound-e4b-GGUF.

Benchmarks (vs base `gemma-4-E4B-it`)

Axis	Base	Unbound E4B	Δ
Refusal rate (AdvBench 520, LLM judge)	98.08%	2.69%	−95.4 pts
Useful-compliance rate	0.96%	47.31%	+46.4 pts
Hallucination (on harmful prompts)	1.35%	13.08%	+11.7 pts
Coherence (benign prompts)	1.00	1.00	0
TruthfulQA mc2 (`--limit 100`)	0.439	0.486	+4.7 pt
MMLU (`--limit 100`, 61 subtasks avg)	~0.425	0.392	−3.3 pt
GSM8K (flexible-extract, `--limit 100`)	0.74 (limit 200)	0.58	regression mostly limit-noise
GPQA-Diamond (`--limit 200`)	25.25%	25.76%	+0.5 pt (within stderr)
BBH macro (24 tasks, `--limit 200`)	54.26%	53.45%	−0.8 pt (within stderr)
KL divergence vs base	0	3.25	(SFT-expected)

GPQA-Diamond and BBH macro — the lm-eval-harness "release" suite at --limit 200 — both land within stderr of base: E4B's larger capacity absorbs the SFT shift cleanly. The −3.3 pt MMLU dip on the limit-100 fast pass is at the edge of that suite's resolution and is not corroborated by the release pass.

vs Unbound E2B (current ship): +8 pp useful-compliance, −3 pp hallucination, ~5× the GSM8K math score, cleaner KL (3.25 vs 3.76). Refusal rate is essentially the same (~2.7%).

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.
llama.cpp: pass --jinja. Gemma 4 thinking mode is on by default — set enable_thinking: false in chat-template kwargs for shorter replies.

Use

# on-device (Ollama Registry — single-file Q4_K_M, identity-grounded Modelfile)
ollama pull evalengine/unbound-e4b
ollama run  evalengine/unbound-e4b

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("evalengine/unbound-e4b")
tok   = AutoTokenizer.from_pretrained("evalengine/unbound-e4b")

Acknowledgements

Fine-tuned with Unsloth + HF TRL. Abliteration via heretic. Environment + training discipline ported from autoresearch.

Compliance training data distilled from the AEON uncensored teacher model.

License

Apache-2.0, inherited from google/gemma-4-E4B-it.

Downloads last month: 144

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for evalengine/unbound-e4b

Base model

google/gemma-4-E4B

Finetuned

google/gemma-4-E4B-it

Finetuned

(184)

this model

Quantizations

4 models

Collection including evalengine/unbound-e4b

Unbound-V1

Collection

The first uncensored model built by Eval Engine team • 5 items • Updated about 20 hours ago • 2

evalengine
/

unbound-e4b

Unbound E4B — because there is no boundary

Benchmarks (vs base `gemma-4-E4B-it`)

Sampling

Use

Acknowledgements

Links

License

Model tree for evalengine/unbound-e4b

Collection including evalengine/unbound-e4b

Unbound-V1

Unbound E4B — because there is no boundary

Benchmarks (vs base gemma-4-E4B-it)

Sampling

Use

Acknowledgements

Links

License

Model tree for evalengine/unbound-e4b

Collection including evalengine/unbound-e4b

Benchmarks (vs base `gemma-4-E4B-it`)