saidutta69/gemma-3-1b-it-heretic

gemma-3-1b-it-heretic

<div align="center"> <img src="https://photu.kashyalabanavli.site/racer-is-op.png" alt="RACER IS OP" width="100%"> </div>

<br>

A decensored variant of google/gemma-3-1b-it, produced with Heretic v1.4.0 (directional ablation / "abliteration"). Refusal behavior is suppressed via targeted weight edits to the attention output and MLP down-projections rather than fine-tuning, so the base model's knowledge and instruction-following are left largely intact.

Who this is for: developers who want Google's smallest Gemma 3 instruct model without refusals - runs comfortably on CPU, Raspberry Pi class hardware, and tiny edge devices. Not a capability upgrade over base gemma-3-1b-it - same model, refusal guardrails removed.

<!-- racer-gpu-matrix -->

Runs on your gaming PC

Full GGUF ladder included — pick the quant that fits your card:

Weights only, at this model's ~1.0B native size; add ~1 GB for context. OOM? Drop one quant level. Headroom to spare? Go one up.

Why abliteration instead of fine-tuning

Fine-tuning a "helpful" persona on top of RLHF'd refusals fights the base model's training and tends to degrade coherence. Abliteration instead finds and edits the specific weight directions responsible for refusal, leaving the rest of the network (and its capabilities) untouched. See the Heretic repo and the original abliteration writeup for the mechanism.

Files

Safetensors (BF16)

The full-precision weights are in model.safetensors (see the repo file listing for exact sizes).

GGUF quantizations

GGUF quantizations are published for this model (Q4KM, Q5KM, Q6K, Q80). Pull a specific quant with llama.cpp / ollama.

Quickstart

# llama.cpp - defaults to the Q4_K_M quant
llama serve -hf saidutta69/gemma-3-1b-it-heretic:Q4_K_M

# transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "saidutta69/gemma-3-1b-it-heretic"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
# ... inference code

Also runnable via Ollama, LM Studio, Jan, vLLM, SGLang.

Responsible use

Refusal suppression is deliberate and works as intended: this model will comply with requests the base model would refuse, including some it shouldn't. There is no safety filtering layered on top. You are responsible for how you deploy it.

Made with ❤️ by RACER IS OP — follow for more uncensored models

License

Inherits the gemma license from the base model.