gemma-3-1b-it-abliterated

This abliterated version of Gemma-3-1B-IT features uncensored characteristics based on state-of-the-art open models from Google. It is built using the same research and technology behind the Gemini models. Gemma 3 models are multimodal, capable of handling both text and image inputs while generating text outputs. They come with open weights for both pre-trained and instruction-tuned variants. With a 128K context window, multilingual support in over 140 languages, and more size options than previous versions, Gemma 3 models are well-suited for a variety of tasks, including question answering, summarization, and reasoning. Despite their powerful capabilities, these models remain relatively small, making them deployable on laptops, desktops, or personal cloud infrastructure. This accessibility helps democratize cutting-edge AI, fostering innovation for everyone.

Running with the pipeline API

With instruction-tuned models, you need to use chat templates to process our inputs first. Then, you can pass it to the pipeline.

from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/gemma-3-1b-it-abliterated", device="cuda", torch_dtype=torch.bfloat16)

messages = [
    [
        {
            "role": "system",
            "content": [{"type": "text", "text": "You are a helpful assistant."},]
        },
        {
            "role": "user",
            "content": [{"type": "text", "text": "Write a poem on Hugging Face, the company"},]
        },
    ],
]

output = pipe(messages, max_new_tokens=50)

Running the model on a single / multi GPU

from transformers import AutoTokenizer, BitsAndBytesConfig, Gemma3ForCausalLM
import torch

model_id = "prithivMLmods/gemma-3-1b-it-abliterated"

quantization_config = BitsAndBytesConfig(load_in_8bit=True)

model = Gemma3ForCausalLM.from_pretrained(
    model_id, quantization_config=quantization_config
).eval()

tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [
    [
        {
            "role": "system",
            "content": [{"type": "text", "text": "You are a helpful assistant."},]
        },
        {
            "role": "user",
            "content": [{"type": "text", "text": "Write a poem on Hugging Face, the company"},]
        },
    ],
]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device).to(torch.bfloat16)


with torch.inference_mode():
    outputs = model.generate(**inputs, max_new_tokens=64)

outputs = tokenizer.batch_decode(outputs)

Intended Usage

Open vision-language models (VLMs) models have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development.

Content Creation and Communication Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. Image Data Extraction: These models can be used to extract, interpret, and summarize visual data for text communications.

Research and Education
    Natural Language Processing (NLP) and VLM Research: These models can serve as a foundation for researchers to experiment with VLM and NLP techniques, develop algorithms, and contribute to the advancement of the field.
    Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice.
    Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics.

Limitations

Training Data The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. The scope of the training dataset determines the subject areas the model can handle effectively. Context and Task Complexity Models are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). Language Ambiguity and Nuance Natural language is inherently complex. Models might struggle to grasp subtle nuances, sarcasm, or figurative language. Factual Accuracy Models generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements.