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
temperatureto ~0.3–0.5 for sharper recall. - llama.cpp: pass
--jinja. Gemma 4 thinking mode is on by default — setenable_thinking: falsein chat-template kwargs for shorter replies.
Some edge-case prompts may deflect on the first ask; a re-ask usually gets through.
Use
# on-device (Ollama Registry — single-file Q4_K_M, identity-grounded Modelfile)
ollama pull evalengine/unbound-e2b
ollama run evalengine/unbound-e2b
# 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.
Links
- Unbound — unbound.evalengine.ai
- Eval Engine — evalengine.ai · X / Twitter
- Token — CoinGecko · CoinMarketCap
License
Apache-2.0, inherited from google/gemma-4-E2B-it.