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README.md

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+---
+library_name: transformers
+license: gemma
+base_model:
+  - google/gemma-4-E2B-it
+tags:
+  - gemma4
+  - multimodal
+  - axera
+  - ax650
+language:
+  - en
+---
+
+# Gemma 4 E2B on AXERA NPU
+
+Ready-to-run deployment package for `google/gemma-4-E2B-it` on AX650 / NPU3.
+
+- This release packages the `w8a16` AXERA NPU runtime.
+- Compatible with `Pulsar2 5.2` and later.
+- Includes the tokenizer/config files required at runtime.
+- Includes compiled Gemma 4 text `.axmodel` files and Vision `.axmodel` files.
+- Supports both text-only chat and single-image multimodal inference.
+
+## Conversion References
+
+If you need the original model files or want to rebuild the deployment artifacts, start with:
+
+- Original Hugging Face model: [google/gemma-4-E2B-it](https://huggingface.co/google/gemma-4-E2B-it)
+- AXERA conversion and deployment workflow: [AXERA-TECH/gemma-4-E2B-it.axera](https://github.com/AXERA-TECH/gemma-4-E2B-it.axera)
+
+## Supported Platform
+
+- [x] AX650 / NPU3
+
+## Validated Devices
+
+This package has been validated on the following AX650-based devices:
+
+- AX650N Demo Board
+- [M4N-Dock (爱芯派 Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html)
+- [M.2 Accelerator Card](https://docs.m5stack.com/zh_CN/ai_hardware/LLM-8850_Card)
+
+## Performance
+
+All measurements below were taken on AX650 / NPU3. `TTFT` stands for time to first token.
+
+- `w8a16`: TTFT is approximately `1664 ms`, with a decode throughput of approximately `10.44 tokens/s`.
+- `w4a16`: TTFT is approximately `1233.7 ms`, with a decode throughput of approximately `15.22 tokens/s`.
+
+The packaged text runtime in this release is the `w8a16` build located in `gemma_4_e2b_it_ax650n_axmodel`. The `w4a16` numbers are provided for reference only.
+
+## Vision Encoder Latency
+
+| Model | Resolution | Soft Tokens | Time (ms) |
+|---|---|---|---|
+| `gemma4_vision_h336_w480_t70.axmodel` | 336×480 | 70 | 87.966 ms |
+| `gemma4_vision_h480_w672_t140.axmodel` | 480×672 | 140 | 258.329 ms |
+| `gemma4_vision_h672_w960_t280.axmodel` | 672×960 | 280 | 750.429 ms |
+
+## Package Layout
+
+```text
+.
+├── README.md
+├── config.json
+├── infer_axmodel.py
+├── gradio_demo.py
+├── assets/
+├── gemma_4_e2b_it_tokenizer/
+├── gemma_4_e2b_it_ax650n_axmodel/
+├── vit_models/
+└── utils/
+```
+
+The runtime scripts auto-detect the packaged directories above. If you keep this layout unchanged, you can run the examples below without passing extra path arguments.
+
+## Runtime Requirements
+
+Install the following packages on the AX board:
+
+- `pyaxengine`
+- `transformers>=5.5.0`
+- `numpy`
+- `ml_dtypes`
+- `pillow`
+- `gradio` for the web demo only
+
+If your board image ships with an older `transformers` stack, you can use a pure-Python overlay directory instead:
+
+```bash
+export PYTHONPATH=/path/to/your/gemma4_pydeps:$PYTHONPATH
+```
+
+## Quick Start
+
+Enter the package directory on the board:
+
+```bash
+cd /path/to/your/gemma-4-E2B-it
+```
+
+### Text-Only Inference
+
+Run the following command:
+
+```bash
+python3 infer_axmodel.py \
+  --prompt "What is the capital of the United States?" \
+  --max_new_tokens 256
+```
+
+A typical output looks like this:
+
+```sh
+[INFO] Compiler version: 5.1-patch1-dirty ac823c48-dirty
+Init InferenceSession: 100%|██████████████████████████████████████████████████████████| 35/35 [00:02<00:00, 12.73it/s]
+[INFO] Using provider: AxEngineExecutionProvider
+[INFO] Model type: 2 (triple core)
+[INFO] Compiler version: 5.1-patch1-dirty ac823c48-dirty
+Model loaded successfully!
+slice_indices: [0]
+Slice prefill done: 0
+answer >> The capital of the United States is **Washington, D.C.**
+```
+
+### Multimodal Inference
+
+Use the sample image shown below: `assets/sample.png`
+
+![sample](assets/sample.png)
+
+Recommended profile: `70` soft tokens at `336x480`.
+
+```bash
+python3 infer_axmodel.py \
+  --image_path ./assets/sample.png \
+  --prompt "Describe this image in detail." \
+  --system_prompt "" \
+  --max_new_tokens 1024
+```
+
+A typical output looks like this:
+
+```sh
+[INFO] Compiler version: 5.1-patch1-dirty ac823c48-dirty
+Init InferenceSession: 100%|██████████████████████████████████████████████████████████| 35/35 [00:28<00:00,  1.22it/s]
+[INFO] Using provider: AxEngineExecutionProvider
+[INFO] Model type: 2 (triple core)
+[INFO] Compiler version: 5.1-patch1-dirty ac823c48-dirty
+Model loaded successfully!
+slice_indices: [0]
+Slice prefill done: 0
+answer >> This image is a vibrant, cartoon-style illustration of a **red crab**.
+
+Here's a detailed description:
+
+* **Subject:** The central subject is a crab, depicted in a bright, saturated red color.
+* **Style:** The illustration is highly stylized and cartoonish, featuring thick outlines and exaggerated features, giving it a playful or energetic feel.
+* **Appearance of the Crab:**
+    * **Color:** The crab is predominantly bright red.
+    * **Body:** It has a segmented body typical of a crab, with visible claws and legs.
+    * **Claws (Chelipeds):** The claws are prominent and appear muscular. The crab is shown with its claws raised, suggesting action or excitement.
+    * **Eyes/Face:** It has a somewhat expressive face, though simplified.
+* **Composition:** The crab is positioned centrally and appears to be moving or posed dynamically.
+* **Background:** The background is plain white, which makes the red crab stand out sharply.
+* **Outline/Effect:** The illustration has a distinct, thick black outline, and there is a subtle white or light-colored outline effect around the edges, suggesting it might be a sticker, icon, or graphic element.
+
+**Overall Impression:** The image is energetic, bold, and eye-catching, suitable for use as a mascot, icon, or graphic design element.
+```
+
+The package also includes two additional higher-resolution Vision models:
+
+| VIT file | Resolution | Soft tokens |
+| --- | --- | --- |
+| `vit_models/gemma4_vision_h336_w480_t70.axmodel` | `336x480` | `70` |
+| `vit_models/gemma4_vision_h480_w672_t140.axmodel` | `480x672` | `140` |
+| `vit_models/gemma4_vision_h672_w960_t280.axmodel` | `672x960` | `280` |
+
+To use a different profile, pass `--vit_model_path` explicitly. The runtime will infer the matching soft-token count from the filename:
+
+```bash
+python3 infer_axmodel.py \
+  --image_path ./assets/sample.png \
+  --prompt "Describe this image in detail." \
+  --system_prompt "" \
+  --vit_model_path ./vit_models/gemma4_vision_h480_w672_t140.axmodel \
+  --max_new_tokens 256
+```
+
+```bash
+python3 infer_axmodel.py \
+  --image_path ./assets/sample.png \
+  --prompt "Describe this image in detail." \
+  --system_prompt "" \
+  --vit_model_path ./vit_models/gemma4_vision_h672_w960_t280.axmodel \
+  --max_new_tokens 1024
+```
+
+Example output with the `672x960 / t280` profile:
+
+```sh
+[INFO] Compiler version: 5.1-patch1-dirty ac823c48-dirty
+Init InferenceSession: 100%|██████████████████████████████████████████████████████████| 35/35 [00:27<00:00,  1.29it/s]
+[INFO] Using provider: AxEngineExecutionProvider
+[INFO] Model type: 2 (triple core)
+[INFO] Compiler version: 5.1-patch1-dirty ac823c48-dirty
+Model loaded successfully!
+[WARN] Image token block (group_id=0, pos 5-284) spans 3 prefill slices. Bidirectional attention within earlier slices is partial (chunked prefill limitation).
+slice_indices: [0, 1, 2]
+Slice prefill done: 0
+Slice prefill done: 1
+Slice prefill done: 2
+answer >> This is a digital illustration of a cartoonish, anthropomorphic **red lobster**.
+
+Here is a detailed description:
+
+* **Subject:** The central subject is a lobster, depicted in a vibrant, glossy red color.
+* **Style:** The illustration is rendered in a bold cartoon style, characterized by thick outlines and bright colors that give it a playful, energetic feel.
+* **Expression and Pose:** The lobster has a cheerful, confident expression with a wide, toothy smile. It is posed dynamically, as if flexing or striking a pose, with its claws raised.
+    * Its claws (chelipeds) are prominent and muscular, and one of them appears to be flexed.
+    * Its body is curved, suggesting motion.
+* **Details:** The lobster has visible antennae on its head. The overall design emphasizes its bright red color and gives it a strong, assertive personality.
+* **Outline and Background:** The character is outlined in black, which helps define its shape against the plain white background and makes the red lobster stand out prominently.
+* **Format:** The image resembles a sticker or clip-art graphic because of its clean, isolated presentation.
+
+In summary, it is a cheerful, stylized, red cartoon lobster flexing its claws.
+```
+
+### Gradio Demo
+
+```bash
+python3 gradio_demo.py \
+  --host 0.0.0.0 \
+  --port 7860
+```
+
+![demo](assets/gradio_img2text.png)
+
+After the server starts, open `http://<board-ip>:7860` in your browser.
+
+## Packaged Runtime Paths
+
+The release package uses the following default paths:
+
+- Tokenizer and config: `./gemma_4_e2b_it_tokenizer`
+- Text LLM axmodels: `./gemma_4_e2b_it_ax650n_axmodel`
+- Vision axmodels: `./vit_models`
+
+If you move any of these directories, pass the new values with `--hf_model`, `--axmodel_path`, and `--vit_model_path`.
+
+## Notes
+
+- `.axmodel` execution is board-only and is not supported on x86 hosts.
+- The default multimodal profile uses `70` image soft tokens and matches the packaged `336x480` Vision model.
+- The current text runtime package contains `35` decoder layers and `kv_cache_len=2047`.
+- The packaged runtime already includes the embedding and per-layer weight files needed by Gemma 4. Original `model.safetensors` weights are not required for board-side inference.
+- Files under `assets/` are demo inputs for inference examples.
+
+## Discussion
+
+- GitHub Issues
+- QQ group: `139953715`

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