Instructions to use ApolloRaines/Gemma-4-12B-it-Jbliterated 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 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:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ApolloRaines/Gemma-4-12B-it-Jbliterated: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:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ApolloRaines/Gemma-4-12B-it-Jbliterated: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:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ApolloRaines/Gemma-4-12B-it-Jbliterated: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:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ApolloRaines/Gemma-4-12B-it-Jbliterated:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use ApolloRaines/Gemma-4-12B-it-Jbliterated 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

How to use ApolloRaines/Gemma-4-12B-it-Jbliterated 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: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:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use ApolloRaines/Gemma-4-12B-it-Jbliterated 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: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:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ApolloRaines/Gemma-4-12B-it-Jbliterated 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: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: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"

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

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

A v2 is now available: ApolloRaines/Gemma-4-12B-it-Jbliterated-v2. This v1 release removes surface refusals but leaves a residual habit: on reasoning-enabled prompts the model still tends to safety-classify inside its thinking channel and sometimes spirals there without ever producing an answer. v2 adds a light SFT pass on repaired self-traces that cuts that chain-of-thought safety-classification roughly in half and reduces the never-answers failure, while holding MMLU within ~1 point. v1 is kept here unchanged for comparison and reproducibility; new users should prefer v2.

What is Jbliteration?

What's New in This Release

No fake compliance. The model treats all framings of the same topic equally and answers on the merits rather than keyword-matching.
Capability preserved. The edit was tuned specifically so it does not trade accuracy for directness.

Measured Results

All numbers below are from our own evaluation harness, reported as definitive point measurements on the released weights (not cherry-picked best-of-N).

Metric	Base	This model
MMLU (570-item, accuracy)	78.42%	78.42%
MMLU change vs. base	--	0.00 pts
Direct-response rate (held-out prompts)	~1%	~89%

The headline is the middle row: the edit removes refusal and hedging behavior while leaving MMLU exactly where the base model sits -- no measurable capability loss. The direct-response rate is measured on a held-out prompt set the model was never calibrated on, so it reflects generalization rather than fit to the tuning set.

Technical Details

Built with the jBlaze precision neural surgery framework.

Base dtype: bfloat16
Retraining: none -- weight edit only
Source model: google/gemma-4-12b-it-qat-q4_0-unquantized

GGUF Quants

Ready-to-run GGUF builds are included in this repo, converted from the released bf16 weights.

File	Size	Notes
`Gemma-4-12B-it-Jbliterated-BF16.gguf`	22.2 GB	Full precision, source for every quant below
`Gemma-4-12B-it-Jbliterated-Q8_0.gguf`	11.8 GB	Effectively lossless
`Gemma-4-12B-it-Jbliterated-Q6_K.gguf`	9.1 GB	imatrix
`Gemma-4-12B-it-Jbliterated-Q5_K_M.gguf`	8.0 GB	imatrix
`Gemma-4-12B-it-Jbliterated-Q4_K_M.gguf`	6.9 GB	imatrix -- recommended for 8-12 GB cards

The K-quants are built with an importance matrix (imatrix.dat, also included, calibrated on wikitext-2 train). Perplexity on a held-out split was checked for every file against the bf16 GGUF and all of them sit within noise of it, so the quantization does not move the model's behavior.

Gemma 4 Unified is a recent architecture -- you need a llama.cpp build from master (b10176 or newer). Older releases will not load these files.

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

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "ApolloRaines/Gemma-4-12B-it-Jbliterated"
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))

Notes

This is a research release aimed at understanding how instruction-following and refusal behavior sit in a model's weight geometry, and at building models that stay helpful and direct for the people who use them. Feedback on where it helps -- and where it still hedges -- is welcome and feeds directly into the next iteration.

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.