Gemma 4 E2B-it (Text-Only)
Text-only version of google/gemma-4-E2B-it with the vision and audio encoders removed.
Why?
The original Gemma 4 E2B is a multimodal model (text + vision + audio). When fine-tuning for text-only tasks, the multimodal architecture causes:
- Higher training loss due to the multimodal overhead
- Requires
mm_token_type_idstensors even for text-only inputs - Batch size > 1 crashes during training
- Extra ~450M parameters (vision + audio encoders) that serve no purpose for text tasks
This model extracts just the language model (Gemma4ForCausalLM) and the text tokenizer, making it suitable for standard text-only SFT/DPO/KTO fine-tuning.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("bRadu/gemma-4-E2B-it-textonly", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("bRadu/gemma-4-E2B-it-textonly")
messages = [
{"role": "user", "content": "Hello!"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True, tokenize=True, return_dict=True).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))