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evalengine/unbound-e2b

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face

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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 & 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 in-browser) are at `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 + 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-E2B-it.

Unbound E2B — because there is no boundary

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

Sampling

Use

Acknowledgements

Links

License

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