| --- |
| license: apache-2.0 |
| license_link: https://ai.google.dev/gemma/docs/gemma_4_license |
| language: |
| - zh |
| - en |
| base_model: |
| - google/gemma-4-e4b-it |
| library_name: transformers |
| pipeline_tag: any-to-any |
| tags: |
| - gemma4 |
| - tool-calling |
| - traditional-chinese |
| - taiwan |
| --- |
| |
| # ACE-3-E4B-Preview |
|
|
|  |
|  |
|
|
| ## Model Description |
|
|
| **ACE-3-E4B-Preview** is a preview release of the edge-sized member of APMIC's ACE-3 model family, intended for |
| Traditional Chinese (Taiwan) enterprise scenarios and agentic workflows on resource-constrained hardware. |
|
|
| The model is based on **[google/gemma-4-e4b-it](https://huggingface.co/google/gemma-4-e4b-it)**, a dense multimodal |
| model with about 4.5B effective parameters (8B including Per-Layer Embeddings — the "E" in E4B stands for |
| "effective"). It accepts text, image and audio input, generates text, and supports a 128K-token context window. |
|
|
| --- |
|
|
| ## Model Details |
|
|
| - **Developed by:** [APMIC](https://www.apmic.ai/) |
| - **Model type:** Gemma4ForConditionalGeneration (Transformers), dense with Per-Layer Embeddings (~4.5B effective / 8B total) |
| - **Base model:** google/gemma-4-e4b-it |
| - **Modalities:** text, image and audio in; text out |
| - **Context length:** 128K tokens |
| - **Language(s) (NLP):** Traditional Chinese & English |
| - **Weights precision:** bfloat16 (not quantized) |
| - **License:** Apache 2.0 (inherited from Gemma 4) |
|
|
| --- |
|
|
| ## Usage |
|
|
| ```python |
| messages = [{"role": "user", "content": "請用繁體中文簡單說明什麼是邊緣運算。"}] |
| |
| prompt = tokenizer.apply_chat_template( |
| messages, |
| tokenize=False, |
| add_generation_prompt=True, |
| enable_thinking=False, # True to let the model produce a reasoning block first |
| ) |
| ``` |
|
|
| ### Transformers |
|
|
| ```python |
| import torch |
| from transformers import AutoProcessor, AutoModelForImageTextToText |
| |
| model_id = "APMIC/ACE-3-E4B-Preview" |
| |
| processor = AutoProcessor.from_pretrained(model_id) |
| model = AutoModelForImageTextToText.from_pretrained( |
| model_id, torch_dtype=torch.bfloat16, device_map="auto" |
| ) |
| |
| messages = [{"role": "user", "content": "請用繁體中文簡單說明什麼是邊緣運算。"}] |
| inputs = processor.apply_chat_template( |
| messages, add_generation_prompt=True, enable_thinking=False, |
| tokenize=True, return_dict=True, return_tensors="pt", |
| ).to(model.device) |
| |
| out = model.generate(**inputs, max_new_tokens=512) |
| print(processor.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)) |
| ``` |
|
|
| ### Recommended sampling |
|
|
| The bundled `generation_config.json` uses `temperature=1.0`, `top_p=0.95`, `top_k=64`. These follow the base model's |
| recommended settings. |
|
|
| ### Serving |
|
|
| The model can be served with any inference engine that supports Gemma 4 (for example vLLM). Because the weights are |
| bfloat16, plan for roughly 16 GB of GPU memory for the weights alone, plus KV cache. |
|
|
| --- |
|
|
| ## Intended Use and Limitations |
|
|
| **Intended for** |
|
|
| - Traditional Chinese (Taiwan) assistants and enterprise applications on edge or single-GPU deployments |
| - Function-calling and agent frameworks that need a small, locally deployable model |
| - Research and evaluation of the ACE-3 model family |
|
|
| **Limitations** |
|
|
| - This is a preview release; behavior may change in later ACE-3 versions. |
| - Like all LLMs, the model can produce incorrect or fabricated content. For high-risk use (financial, legal, |
| medical), keep a human review or enterprise control layer in place. |
| - Tool-call outputs should be validated (schema and argument checks) before being executed. |
| - Performance on languages other than Traditional Chinese and English has not been specifically evaluated. |
|
|
| --- |
|
|
| ## License |
|
|
| This model is a derivative of Gemma 4 and is distributed under the **Apache License 2.0**, following the base |
| model's [Gemma 4 license](https://ai.google.dev/gemma/docs/gemma_4_license). |
|
|
