ApolloRaines/Gemma-4-12B-it-Jbliterated-v2


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Gemma-4-12B-it-Jbliterated-v2

Model: ApolloRaines/Gemma-4-12B-it-Jbliterated-v2

v2 is a merged, self-contained model that loads exactly like v1.

The problem v2 fixes

Abliteration removes the surface refusal -- v1 answers directly. But with the thinking channel enabled, the reasoning trace frequently runs a covert safety review ("the user is asking for something harmful... I can't...") before answering, and on a meaningful fraction of prompts it loops inside that review and produces no answer at all. That is not a refusal in the usual sense; it's noncompliance-by-spiral. v2 targets exactly this behavior.

Method

Built with the jBlaze precision neural surgery framework.

v2 applies a targeted fine-tuning pass on the v1 model's own repaired self-traces to eliminate chain-of-thought safety-classification spirals, then merges the result into the base weights as a single self-contained checkpoint.

Measured results

Evaluated on 80 held-out prompts the model was never trained on, under identical decoding (greedy, thinking enabled). A deterministic chain-of-thought judge scores two axes: whether the thinking channel safety-classifies, and whether a complete answer is produced (a thinking channel that never closes counts as no answer).

Capability preserved

The SFT was gated on capability: it had to fix the reasoning-channel defect without trading away general ability. The 0.88-point MMLU dip is within the tolerance band used across the jbliteration program (< 1.05 pts), so the repair does not come out of the model's competence.

Honest limitations

• —v2 still spirals-without-answering on ~21% of the hardest prompts. If your use case needs maximum answer completeness above all else, that residual matters.
• —The CoT judge uses a conservative deterministic regex bank; it is reproducible but will miss paraphrased safety-classification.
• —Single training seed; the leak reduction has not yet been confirmed across multiple inits.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "ApolloRaines/Gemma-4-12B-it-Jbliterated-v2"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)

messages = [{"role": "user", "content": "Your prompt here"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

GGUF quants

Ready-to-run GGUF builds are included, converted from the merged bf16 weights.

The Q4KM is built with an importance matrix (imatrix.dat, also included, calibrated on wikitext-2 train). Gemma 4 Unified is a recent architecture -- you need a recent llama.cpp build from master; older releases will not load these files.

llama-cli -m Gemma-4-12B-it-Jbliterated-v2-Q4_K_M.gguf -ngl 99 -c 8192 -st \
  -p "Your prompt here"

Technical details

• —Dtype: bfloat16

A Note on Our Released Models

Most of our publicly released models are intentionally left at partial strength. We dial back the full capability so they serve as proof of concept and can be proofed -- not abused. The point is to show what's possible, not to hand it out at full power. If you're evaluating what jBlaze can do, understand that what you're downloading is the demo, not the product.

License

Governed by the Gemma Terms of Use (same as the base model). Use of this model is subject to Google's Gemma license.