Instructions to use AXERA-TECH/gemma-4-E2B-it-GPTQ-INT4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AXERA-TECH/gemma-4-E2B-it-GPTQ-INT4 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AXERA-TECH/gemma-4-E2B-it-GPTQ-INT4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| import json | |
| from pathlib import Path | |
| from types import SimpleNamespace | |
| from transformers import PreTrainedTokenizerFast | |
| def load_text_runtime_config(model_dir: str): | |
| model_path = Path(model_dir) | |
| with open(model_path / "config.json", encoding="utf-8") as f: | |
| raw_config = json.load(f) | |
| text_config = dict(raw_config.get("text_config") or raw_config) | |
| text_config["model_type"] = text_config.get("model_type", "gemma4_text") | |
| text_config["eos_token_id"] = raw_config.get("eos_token_id", text_config.get("eos_token_id")) | |
| text_config["image_token_id"] = raw_config.get("image_token_id") | |
| text_config["vision_config"] = raw_config.get("vision_config") | |
| text_config["vision_soft_tokens_per_image"] = raw_config.get("vision_soft_tokens_per_image") | |
| return SimpleNamespace(**text_config) | |
| def load_tokenizer(model_dir: str): | |
| model_path = Path(model_dir) | |
| with open(model_path / "tokenizer_config.json", encoding="utf-8") as f: | |
| tokenizer_config = json.load(f) | |
| init_kwargs = { | |
| "tokenizer_file": str(model_path / "tokenizer.json"), | |
| "bos_token": tokenizer_config.get("bos_token"), | |
| "eos_token": tokenizer_config.get("eos_token"), | |
| "pad_token": tokenizer_config.get("pad_token"), | |
| "unk_token": tokenizer_config.get("unk_token"), | |
| "mask_token": tokenizer_config.get("mask_token"), | |
| "padding_side": tokenizer_config.get("padding_side", "left"), | |
| } | |
| tokenizer = PreTrainedTokenizerFast(**{k: v for k, v in init_kwargs.items() if v is not None}) | |
| chat_template_path = model_path / "chat_template.jinja" | |
| if chat_template_path.exists(): | |
| tokenizer.chat_template = chat_template_path.read_text(encoding="utf-8") | |
| return tokenizer | |