base_model:
- gghfez/gemma-3-4b-novision
license: gemma
pipeline_tag: text-generation
library_name: transformers
Gemma 3 4B (abliterated text-only) model card
This is an abliterated text-only version of google/gemma-3-4b-it, created using Baukit.
The vision encoders were removed by gghf. Please note that this model may exhibit a reduced performance.
Model Description
- Original Model: The original Gemma-3-4b-it is a multimodal model released by Google that can process both text and images
- This Version: This version has been modified to use the same architecture as the text-only 1b model, with the vision components removed
- Parameters: 4 billion parameters
- Conversion Process: Vision-related components were stripped while maintaining the text generation capabilities
Usage
You can load and use this model the same way you would use the text-only google/gemma-3-1b-it version:
from transformers import AutoTokenizer, BitsAndBytesConfig, Gemma3ForCausalLM
import torch
model_id = "gghfez/gemma-3-4b-novision"
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)