unbound-e4b

evalengine/unbound-e4b

unbound-e4b is a image-text-to-text model from evalengine. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.

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

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At a glance

Task: Image-Text-to-Text
Library: transformers
License: apache-2.0
Model type: gemma4
Access: Public
Created: May 18, 2026
Updated: May 20, 2026
SHA: ccb509ad

Try a prompt

Base models

google/gemma-4-E4B-it

Model card

From the Hugging Face model README

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.

Task: Image-Text-to-Text
Library: transformers
Type: gemma4
License: apache-2.0
Created: May 18, 2026
Updated: May 20, 2026