Add MLX quantized model with KV cache compression

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,103 @@
+---
+base_model: google/gemma-4-E4B
+library_name: mlx
+tags:
+  - rotorquant
+  - kv-cache-quantization
+  - gemma
+  - gemma4
+  - multimodal
+  - quantized
+  - mlx
+  - 4bit
+license: apache-2.0
+pipeline_tag: image-text-to-text
+---
+
+# Gemma 4 E4B - RotorQuant MLX 4-bit
+
+**4-bit weight-quantized MLX version** of [google/gemma-4-E4B](https://huggingface.co/google/gemma-4-E4B) with RotorQuant KV-cache quantization. Optimized for Apple Silicon inference via the [MLX](https://github.com/ml-explore/mlx) framework. RotorQuant delivers 5.3x faster prefill and 28% faster decode compared to TurboQuant. A good balance between model quality and memory efficiency.
+
+Approximate model size: **~2.3 GB**
+
+## Model Specifications
+
+| Property | Value |
+|---|---|
+| **Base Model** | [google/gemma-4-E4B](https://huggingface.co/google/gemma-4-E4B) |
+| **Parameters** | ~4 billion |
+| **Architecture** | Dense transformer |
+| **Modality** | Multimodal: image + text input, text output |
+| **License** | Apache 2.0 |
+| **Weight Quantization** | 4-bit (~2.3 GB) |
+| **KV-Cache Quantization** | RotorQuant |
+| **Framework** | MLX (Apple Silicon) |
+
+## Quickstart
+
+```python
+import mlx.core as mx
+from mlx_lm import load, generate
+
+model, tokenizer = load("majentik/gemma-4-E4B-RotorQuant-MLX-4bit")
+
+prompt = "The history of artificial intelligence began"
+response = generate(model, tokenizer, prompt=prompt, max_tokens=512)
+print(response)
+```
+
+For multimodal usage with images:
+
+```python
+from mlx_vlm import load, generate
+
+model, processor = load("majentik/gemma-4-E4B-RotorQuant-MLX-4bit")
+
+prompt = "Describe the contents of this image."
+output = generate(model, processor, prompt=prompt, image="path/to/image.jpg", max_tokens=512)
+print(output)
+```
+
+## What is RotorQuant?
+
+[RotorQuant](https://github.com/scrya-com/rotorquant) is a high-performance KV-cache quantization method that achieves significantly better throughput than TurboQuant. Combined with 4-bit weight quantization in MLX, this provides a dual compression strategy with superior KV-cache performance: smaller model weights plus faster compressed KV cache for efficient long-context generation.
+
+Key advantages over TurboQuant:
+- **5.3x faster prefill**
+- **28% faster decode**
+- Equivalent memory savings
+
+## KV-Cache Quantization Comparison
+
+| Method | Prefill Speed | Decode Speed | Memory Savings | Reference |
+|---|---|---|---|---|
+| **TurboQuant** | 1x (baseline) | 1x (baseline) | High | [arXiv: 2504.19874](https://arxiv.org/abs/2504.19874) |
+| **RotorQuant** | **5.3x faster** | **28% faster** | High | [GitHub](https://github.com/scrya-com/rotorquant) |
+
+## Memory Estimates (Gemma 4 E4B)
+
+| Precision | Approximate Size | MLX Variant |
+|---|---|---|
+| FP16 (original) | ~8 GB | -- |
+| 8-bit quantized | ~4 GB | [RotorQuant-MLX-8bit](https://huggingface.co/majentik/gemma-4-E4B-RotorQuant-MLX-8bit) |
+| **4-bit quantized** | **~2.3 GB** | **This model** |
+| 2-bit quantized | ~1.2 GB | [RotorQuant-MLX-2bit](https://huggingface.co/majentik/gemma-4-E4B-RotorQuant-MLX-2bit) |
+
+## Hardware Requirements
+
+This model requires approximately 2.3 GB of unified memory. Recommended hardware:
+- Apple M1 (8 GB+)
+- Apple M2 (8 GB+)
+- Apple M3 (8 GB+)
+- Apple M4 (8 GB+)
+- Any Apple Silicon Mac with 8 GB+ unified memory
+
+## See Also
+
+- [google/gemma-4-E4B](https://huggingface.co/google/gemma-4-E4B) -- Base model
+- [majentik/gemma-4-E4B-RotorQuant](https://huggingface.co/majentik/gemma-4-E4B-RotorQuant) -- RotorQuant KV-cache only (transformers)
+- [majentik/gemma-4-E4B-RotorQuant-MLX-8bit](https://huggingface.co/majentik/gemma-4-E4B-RotorQuant-MLX-8bit) -- MLX 8-bit variant
+- [majentik/gemma-4-E4B-RotorQuant-MLX-2bit](https://huggingface.co/majentik/gemma-4-E4B-RotorQuant-MLX-2bit) -- MLX 2-bit variant
+- [majentik/gemma-4-E4B-TurboQuant-MLX-4bit](https://huggingface.co/majentik/gemma-4-E4B-TurboQuant-MLX-4bit) -- TurboQuant MLX 4-bit variant
+- [RotorQuant GitHub](https://github.com/scrya-com/rotorquant)
+- [MLX Framework](https://github.com/ml-explore/mlx)

config.json

@@ -0,0 +1,204 @@
+{
+    "architectures": [
+        "Gemma4ForConditionalGeneration"
+    ],
+    "audio_config": {
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+        "hidden_act": "silu",
+        "hidden_size": 1024,
+        "id2label": {
+            "0": "LABEL_0",
+            "1": "LABEL_1"
+        },
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+        "is_encoder_decoder": false,
+        "label2id": {
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+        "model_type": "gemma4_audio",
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+        "num_hidden_layers": 12,
+        "output_attentions": false,
+        "output_hidden_states": false,
+        "output_proj_dims": 1536,
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+        "residual_weight": 0.5,
+        "return_dict": true,
+        "rms_norm_eps": 1e-06,
+        "subsampling_conv_channels": [
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+    "boa_token_id": 256000,
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+    "dtype": "bfloat16",
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+    "eos_token_id": 1,
+    "image_token_id": 258880,
+    "initializer_range": 0.02,
+    "model_type": "gemma4",
+    "quantization": {
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+        "bits": 4,
+        "mode": "affine"
+    },
+    "quantization_config": {
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+        "bits": 4,
+        "mode": "affine"
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+    "text_config": {
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+    "transformers_version": "5.5.0.dev0",
+    "video_token_id": 258884,
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+        "architectures": null,
+        "attention_bias": false,
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+        "global_head_dim": 64,
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+        "hidden_activation": "gelu_pytorch_tanh",
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+        "id2label": {
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+            "1": "LABEL_1"
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+        "is_encoder_decoder": false,
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+        "use_clipped_linears": true
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+    "vision_soft_tokens_per_image": 280
+}

generation_config.json

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+{
+  "bos_token_id": 2,
+  "do_sample": true,
+  "eos_token_id": 1,
+  "pad_token_id": 0,
+  "temperature": 1.0,
+  "top_k": 64,
+  "top_p": 0.95,
+  "transformers_version": "5.5.0.dev0"
+}

model.safetensors

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processor_config.json

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+{
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+  "image_processor": {
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+    "do_normalize": false,
+    "do_rescale": true,
+    "do_resize": true,
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+    "image_std": [
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+      "width": 224
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+  "processor_class": "Gemma4Processor",
+  "feature_extractor": {
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+    "num_mel_filters": 128,
+    "fft_length": 512,
+    "hop_length": 160,
+    "chunk_duration": 8.0,
+    "overlap_duration": 1.0
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+}

tokenizer.json

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tokenizer_config.json

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+{
+  "audio_token": "<|audio|>",
+  "backend": "tokenizers",
+  "boa_token": "<|audio>",
+  "boi_token": "<|image>",
+  "bos_token": "<bos>",
+  "eoa_token": "<audio|>",
+  "eoc_token": "<channel|>",
+  "eoi_token": "<image|>",
+  "eos_token": "<eos>",
+  "eot_token": "<turn|>",
+  "escape_token": "<|\"|>",
+  "etc_token": "<tool_call|>",
+  "etd_token": "<tool|>",
+  "etr_token": "<tool_response|>",
+  "extra_special_tokens": [
+    "<|video|>"
+  ],
+  "image_token": "<|image|>",
+  "is_local": true,
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "model_specific_special_tokens": {
+    "audio_token": "<|audio|>",
+    "boa_token": "<|audio>",
+    "boi_token": "<|image>",
+    "eoa_token": "<audio|>",
+    "eoc_token": "<channel|>",
+    "eoi_token": "<image|>",
+    "eot_token": "<turn|>",
+    "escape_token": "<|\"|>",
+    "etc_token": "<tool_call|>",
+    "etd_token": "<tool|>",
+    "etr_token": "<tool_response|>",
+    "image_token": "<|image|>",
+    "soc_token": "<|channel>",
+    "sot_token": "<|turn>",
+    "stc_token": "<|tool_call>",
+    "std_token": "<|tool>",
+    "str_token": "<|tool_response>",
+    "think_token": "<|think|>"
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+  "pad_token": "<pad>",
+  "padding_side": "left",
+  "processor_class": "Gemma4Processor",
+  "soc_token": "<|channel>",
+  "sot_token": "<|turn>",
+  "stc_token": "<|tool_call>",
+  "std_token": "<|tool>",
+  "str_token": "<|tool_response>",
+  "think_token": "<|think|>",
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>"
+}