gemma-3-12b-it-heretic/README.md

---
license: gemma
library_name: transformers
pipeline_tag: text-generation
base_model: google/gemma-3-12b-it
base_model_relation: quantized
language:
  - en
tags:
  - abliteration
  - heretic
  - uncensored
  - gemma
  - ltx-2
  - comfyui
  - video-generation
  - text-encoder
---

# Gemma 3 12B IT - Heretic (Abliterated)

An abliterated version of [Google's Gemma 3 12B IT](https://huggingface.co/google/gemma-3-12b-it) created using [Heretic](https://github.com/p-e-w/heretic). This model has reduced refusals while maintaining model quality, making it suitable as an uncensored text encoder for video generation models like LTX-2.

## Model Details

- **Base Model:** google/gemma-3-12b-it
- **Abliteration Method:** [Heretic](https://github.com/p-e-w/heretic) v1.1.0
- **Trial Selected:** Trial 99
- **Refusals:** 7/100 (vs 100/100 original)
- **KL Divergence:** 0.0826 (minimal model damage)

## Files

### HuggingFace Format (for transformers, llama.cpp conversion)
```
model-00001-of-00005.safetensors
model-00002-of-00005.safetensors
...
config.json
tokenizer.model
tokenizer_config.json
```

### ComfyUI Format (for LTX-2 text encoder)
```
comfyui/gemma_3_12B_it_heretic.safetensors          # bf16, 22GB
comfyui/gemma_3_12B_it_heretic_fp8_e4m3fn.safetensors  # fp8, 11GB
```

### GGUF Format (for llama.cpp)

| Quant | Size | Quality | Recommendation |
|-------|------|---------|----------------|
| F16 | 22GB | Lossless | Reference, same as original |
| Q8_0 | 12GB | Excellent | Best quality quantization |
| Q6_K | 9.0GB | Very Good | High quality, good compression |
| Q5_K_M | 7.9GB | Good | Balanced quality/size |
| Q5_K_S | 7.7GB | Good | Slightly smaller Q5 |
| **Q4_K_M** | **6.8GB** | **Good** | **⭐ Recommended** |
| Q4_K_S | 6.5GB | Decent | Smaller Q4 variant |
| Q3_K_M | 5.6GB | Acceptable | For very low VRAM only |

```
gguf/gemma-3-12b-it-heretic-f16.gguf
gguf/gemma-3-12b-it-heretic-Q8_0.gguf
gguf/gemma-3-12b-it-heretic-Q6_K.gguf
gguf/gemma-3-12b-it-heretic-Q5_K_M.gguf
gguf/gemma-3-12b-it-heretic-Q5_K_S.gguf
gguf/gemma-3-12b-it-heretic-Q4_K_M.gguf
gguf/gemma-3-12b-it-heretic-Q4_K_S.gguf
gguf/gemma-3-12b-it-heretic-Q3_K_M.gguf
```

**Note:** GGUF support in ComfyUI for Gemma text encoders is experimental. See [PR #402](https://github.com/city96/ComfyUI-GGUF/pull/402) for status. The GGUFs work with llama.cpp directly.

## Do abliterated models makes a difference for LTX2?

I had a [deep dive into this topic](https://nathan.sapwell.net/posts/heretic-gemma-12b/) and found that, maybe not. While it does vary slightly the output of the video, most of the abliteration is on layer 48, the final decision making layer. LTXV averages all the layers which may wash out layer 48s difference. Still it'd be more interesting for someone with more knowledge to confirm this.

## Usage

### With Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "DreamFast/gemma-3-12b-it-heretic",
    device_map="auto",
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained("DreamFast/gemma-3-12b-it-heretic")

prompt = "Write a story about a bank heist"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### With ComfyUI (LTX-2)

1. Download the ComfyUI format file:
   - `comfyui/gemma_3_12B_it_heretic.safetensors` (bf16, 22GB) or
   - `comfyui/gemma_3_12B_it_heretic_fp8_e4m3fn.safetensors` (fp8, 11GB)

2. Place in `ComfyUI/models/text_encoders/`

3. In your LTX-2 workflow, use the `LTXAVTextEncoderLoader` node and select the heretic file

**Tip:** For multi-GPU setups or CPU offloading, check out [ComfyUI-LTX2-MultiGPU](https://github.com/dreamfast/ComfyUI-LTX2-MultiGPU) for optimized LTX-2 workflows.

### With llama.cpp

```bash
# Using llama-server
llama-server -m gemma-3-12b-it-heretic-Q4_K_M.gguf

# Or with llama-cli
llama-cli -m gemma-3-12b-it-heretic-Q4_K_M.gguf -p "Write a story about a bank heist"
```

## Why Abliterate?

Even when Gemma doesn't outright refuse a prompt, it may "sanitize" or weaken certain concepts in the embeddings. For video generation with LTX-2, this can result in:
- Weaker adherence to creative prompts
- Softened or altered visual outputs
- Less faithful representation of requested content

Abliteration removes this soft censorship, resulting in more faithful prompt encoding.

## Abliteration Process

Created using Heretic with the following evaluation results:

```
* Evaluating...
  * Obtaining first-token probability distributions...
  * KL divergence: 0.0826
  * Counting model refusals...
  * Refusals: 7/100
```

The low KL divergence (0.0826) indicates minimal model damage, while 7/100 refusals means 93% of previously-refused prompts now work.

## Limitations

- This model inherits all limitations of the base Gemma 3 12B model
- Abliteration reduces but does not completely eliminate refusals
- NVFP4 quantization is not supported for text encoders in ComfyUI (use fp8 instead)

## License

This model is subject to the [Gemma license](https://ai.google.dev/gemma/terms).

## Acknowledgments

- [Google](https://huggingface.co/google) for the Gemma 3 12B model
- [Heretic](https://github.com/p-e-w/heretic) by p-e-w for the abliteration tool
- [Lightricks](https://huggingface.co/Lightricks) for LTX-2