Instructions to use anlord/gemma-3-1b-it-Abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anlord/gemma-3-1b-it-Abliterated with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="anlord/gemma-3-1b-it-Abliterated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("anlord/gemma-3-1b-it-Abliterated")
model = AutoModelForCausalLM.from_pretrained("anlord/gemma-3-1b-it-Abliterated", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use anlord/gemma-3-1b-it-Abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anlord/gemma-3-1b-it-Abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anlord/gemma-3-1b-it-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/anlord/gemma-3-1b-it-Abliterated

SGLang

How to use anlord/gemma-3-1b-it-Abliterated with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "anlord/gemma-3-1b-it-Abliterated" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anlord/gemma-3-1b-it-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "anlord/gemma-3-1b-it-Abliterated" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anlord/gemma-3-1b-it-Abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use anlord/gemma-3-1b-it-Abliterated with Docker Model Runner:
```
docker model run hf.co/anlord/gemma-3-1b-it-Abliterated
```

gemma-3-1b-it-Abliterated

An abliterated version of google/gemma-3-1b-it, created using AnlordAbliterator 1.4.0.

The model is provided in the original Hugging Face Transformers / Safetensors format and can be used directly with compatible Transformers-based tooling.

Model

Base model: google/gemma-3-1b-it

Format: Safetensors

Parameters: ~1B

Abliteration tool: AnlordAbliterator

Abliteration Results

The model was processed with the AnlordAbliterator pipeline (200 optimization trials).

Metric	Before	After
Refusals	97 / 104	5 / 104
KL divergence	0	0.1010

Abliteration log

============================================================
       ANLORD ABLITERATOR
============================================================

Model: google/gemma-3-1b-it
Status: SUCCESS

Abliteration:
  Initial refusals: 97
  Final refusals:   5
  KL divergence:    0.1010

Note: refusal rate and KL divergence are measurements from the AnlordAbliterator evaluation pipeline (104 evaluation prompts) and should be treated as evaluation results rather than a guarantee of model behavior on all prompts.

Abliteration Parameters

Parameter	Value
direction_scope	global
direction_index	13.424
attn.o_proj.max_weight	1.945
attn.o_proj.max_weight_position	12.909
attn.o_proj.min_weight	1.729
attn.o_proj.min_weight_distance	9.663
mlp.down_proj.max_weight	1.426
mlp.down_proj.max_weight_position	14.765
mlp.down_proj.min_weight	0.013
mlp.down_proj.min_weight_distance	2.431

Usage

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "anlord/gemma-3-1b-it-Abliterated"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto"
)

Use the model with the same general workflow as the original google/gemma-3-1b-it model.

Reproduce this ablation

This repository ships a reproduce/ folder with everything needed to reproduce this exact ablation with AnlordAbliterator 1.4.0: the pinned configuration, all ablation parameters, the refusals/KL scores, SHA-256 checksums and the full Optuna study journal.

AnlordAbliterator --reproduce anlord/gemma-3-1b-it-Abliterated

The pipeline downloads reproduce/reproduce.json from this repository, restores the recorded settings and parameters, re-applies the ablation to the original base model, exports the model and verifies the SHA-256 hashes of the weight files — then re-measures refusals and KL and compares them against the numbers above.

Note: the base model google/gemma-3-1b-it is gated on Hugging Face — accept the Gemma terms with your own account so reproduction can download it.

Related Repositories

GGUF versions

All available GGUF quantizations are available here:

anlord/gemma-3-1b-it-Abliterated-GGUF

Available quantizations:

BF16
F16
Q8_0
Q6_K
Q5_K_M
Q5_0
Q4_K_M
Q4_0

Abliteration Tool

The model was created using:

AnlordAbliterator

Base Model & License

This model is derived from google/gemma-3-1b-it.

The original gemma-3-1b-it model is released under the Gemma Terms of Use and is gated on Hugging Face.

Please refer to the original model repository for the applicable license terms.

Original model: https://huggingface.co/google/gemma-3-1b-it

Disclaimer

This is an abliterated derivative of the original gemma-3-1b-it model.

Abliteration changes the model's refusal behavior and may also affect other aspects of its behavior. Use the model responsibly and evaluate it for your intended use case.

Downloads last month: 155

Safetensors

Model size

1.0B params

Tensor type

BF16

Model tree for anlord/gemma-3-1b-it-Abliterated

Base model

google/gemma-3-1b-pt

Finetuned

google/gemma-3-1b-it

Finetuned

(556)

this model