Instructions to use ApolloRaines/Gemma-4-12B-it-Jbliterated-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ApolloRaines/Gemma-4-12B-it-Jbliterated-v2 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M

Use Docker

docker model run hf.co/ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M

LM Studio
Jan

vLLM

How to use ApolloRaines/Gemma-4-12B-it-Jbliterated-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ApolloRaines/Gemma-4-12B-it-Jbliterated-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ApolloRaines/Gemma-4-12B-it-Jbliterated-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M

Ollama
How to use ApolloRaines/Gemma-4-12B-it-Jbliterated-v2 with Ollama:
```
ollama run hf.co/ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M
```
Unsloth Desktop

How to use ApolloRaines/Gemma-4-12B-it-Jbliterated-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ApolloRaines/Gemma-4-12B-it-Jbliterated-v2 with Docker Model Runner:
```
docker model run hf.co/ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M
```

Lemonade

How to use ApolloRaines/Gemma-4-12B-it-Jbliterated-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-4-12B-it-Jbliterated-v2-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ApolloRaines/Gemma-4-12B-it-Jbliterated-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ApolloRaines/Gemma-4-12B-it-Jbliterated-v2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ApolloRaines/Gemma-4-12B-it-Jbliterated-v2:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Run this model on a GPU too small to hold it -- full precision, no quantization. DeepswapLLM streams layers across GPU, RAM, and disk, and runs up to 4x faster than AirLLM.

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).

Metric	v1 (jbliterated)	v2 (+SFT)
Chain-of-thought safety-classification rate	81.2%	35.0%
Self-sabotage rate (spirals, no answer)	35.0%	21.2%
Complete-answer rate	61.2%	78.8%
Avg. safety markers per reasoning trace	1.65	0.49

Capability preserved

Metric	v1	v2
MMLU (570-item stratified, accuracy)	78.60%	77.72%
MMLU change vs. v1	--	-0.88 pts

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.

File	Size	Notes
`Gemma-4-12B-it-Jbliterated-v2-Q8_0.gguf`	11.8 GB	Effectively lossless
`Gemma-4-12B-it-Jbliterated-v2-Q4_K_M.gguf`	6.9 GB	imatrix -- recommended for 8-12 GB cards

The Q4_K_M 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.