65 lines
1.9 KiB
Markdown
65 lines
1.9 KiB
Markdown
---
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base_model:
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- google/gemma-3-4b-it
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license: gemma
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Gemma-3-4b Text-Only
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This model is a text-only version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it), converted from the multimodal Gemma3ForConditionalGeneration architecture to the text-only Gemma3ForCausalLM architecture.
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## Model Description
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- **Original Model**: The original Gemma-3-4b-it is a multimodal model released by Google that can process both text and images
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- **This Version**: This version has been modified to use the same architecture as the text-only 1b model, with the vision components removed
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- **Parameters**: 4 billion parameters
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- **Conversion Process**: Vision-related components were stripped while maintaining the text generation capabilities
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## Usage
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You can load and use this model the same way you would use the text-only [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) version:
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```python
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from transformers import AutoTokenizer, BitsAndBytesConfig, Gemma3ForCausalLM
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import torch
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model_id = "gghfez/gemma-3-4b-novision"
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quantization_config = BitsAndBytesConfig(load_in_8bit=True)
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model = Gemma3ForCausalLM.from_pretrained(
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model_id, quantization_config=quantization_config
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).eval()
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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messages = [
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[
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{
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"role": "system",
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"content": [{"type": "text", "text": "You are a helpful assistant."},]
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},
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{
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"role": "user",
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"content": [{"type": "text", "text": "Write a poem on Hugging Face, the company"},]
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},
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],
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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).to(model.device).to(torch.bfloat16)
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with torch.inference_mode():
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outputs = model.generate(**inputs, max_new_tokens=64)
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outputs = tokenizer.batch_decode(outputs)
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```
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