| --- |
| library_name: transformers |
| base_model: |
| - google/gemma-4-E4B-it |
| --- |
| |
| This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [google/gemma-4-E4B-it](https://huggingface.co/google/gemma-4-E4B-it). |
|
|
| | File path | Size | |
| |------|------| |
| | model.safetensors | 9.5MB | |
|
|
|
|
| ### Example usage: |
|
|
| ```python |
| import torch |
| from transformers import AutoModelForCausalLM, AutoProcessor |
| |
| model_id = "tiny-random/gemma-4e" |
| processor = AutoProcessor.from_pretrained(model_id) |
| model = AutoModelForCausalLM.from_pretrained( |
| model_id, dtype=torch.bfloat16, device_map="auto" |
| ) |
| messages = [ |
| { |
| "role": "user", |
| "content": [ |
| { |
| "type": "audio", |
| "audio": "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/Demos/sample-data/journal1.wav", |
| }, |
| {"type": "text", "text": "Transcribe the following speech segment."}, |
| ], |
| }, |
| { |
| "role": "assistant", |
| "content": [{"type": "text", "text": "Dummy response for audio"}], |
| }, |
| { |
| "role": "user", |
| "content": [ |
| { |
| "type": "image", |
| "url": "https://raw.githubusercontent.com/google-gemma/cookbook/refs/heads/main/Demos/sample-data/GoldenGate.png", |
| }, |
| {"type": "text", "text": "What is shown in this image?"}, |
| ], |
| }, |
| { |
| "role": "assistant", |
| "content": [{"type": "text", "text": "Dummy response for image"}], |
| }, |
| { |
| "role": "user", |
| "content": [ |
| { |
| "type": "video", |
| "video": "https://github.com/bebechien/gemma/raw/refs/heads/main/videos/ForBiggerBlazes.mp4", |
| }, |
| {"type": "text", "text": "Describe this video."}, |
| ], |
| }, |
| ] |
| inputs = processor.apply_chat_template( |
| messages, |
| tokenize=True, |
| return_dict=True, |
| return_tensors="pt", |
| add_generation_prompt=True, |
| ).to(model.device) |
| input_len = inputs["input_ids"].shape[-1] |
| print("input_len:", input_len) |
| outputs = model.generate(**inputs, max_new_tokens=32) |
| response = processor.decode(outputs[0], skip_special_tokens=False) |
| response = response.replace("<|audio|>", "A") |
| response = response.replace("<|image|>", "I") |
| response = response.replace("<|video|>", "V") |
| print(response) |
| ``` |
|
|
| ### Codes to create this repo: |
|
|
| <details> |
| <summary>Click to expand</summary> |
|
|
| ```python |
| import json |
| from pathlib import Path |
| |
| import torch |
| from huggingface_hub import file_exists, hf_hub_download |
| |
| from transformers import ( |
| AutoConfig, |
| AutoModelForCausalLM, |
| AutoProcessor, |
| AutoTokenizer, |
| Gemma4ForConditionalGeneration, |
| GenerationConfig, |
| set_seed, |
| ) |
| |
| source_model_id = "google/gemma-4-E4B-it" |
| save_folder = "/tmp/tiny-random/gemma-4e" |
| |
| processor = AutoProcessor.from_pretrained(source_model_id) |
| processor.save_pretrained(save_folder) |
| |
| with open( |
| hf_hub_download(source_model_id, filename="config.json", repo_type="model"), |
| "r", |
| encoding="utf-8", |
| ) as f: |
| config_json = json.load(f) |
| |
| config_json["audio_config"].update( |
| { |
| "num_attention_heads": 2, |
| "num_hidden_layers": 2, |
| "hidden_size": 64, |
| "output_proj_dims": 32, |
| } |
| ) |
| config_json["text_config"].update( |
| { |
| "global_head_dim": 64, |
| "head_dim": 32, |
| "hidden_size": 8, |
| "hidden_size_per_layer_input": 2, |
| "intermediate_size": 64, |
| "layer_types": [ |
| "sliding_attention", |
| "full_attention", |
| "sliding_attention", |
| "full_attention", |
| ], |
| "num_attention_heads": 8, |
| "num_hidden_layers": 4, |
| "num_key_value_heads": 4, |
| "num_kv_shared_layers": 2, |
| } |
| ) |
| config_json["vision_config"].update( |
| { |
| "num_hidden_layers": 2, |
| "hidden_size": 8, |
| "intermediate_size": 64, |
| "head_dim": 32, |
| "global_head_dim": 32, |
| "num_attention_heads": 4, |
| "num_key_value_heads": 4, |
| } |
| ) |
| |
| with open(f"{save_folder}/config.json", "w", encoding="utf-8") as f: |
| json.dump(config_json, f, indent=2) |
| |
| config = AutoConfig.from_pretrained( |
| save_folder, |
| trust_remote_code=True, |
| ) |
| print(config) |
| |
| torch.set_default_dtype(torch.bfloat16) |
| model = Gemma4ForConditionalGeneration(config) |
| torch.set_default_dtype(torch.float32) |
| if file_exists( |
| filename="generation_config.json", repo_id=source_model_id, repo_type="model" |
| ): |
| model.generation_config = GenerationConfig.from_pretrained( |
| source_model_id, |
| trust_remote_code=True, |
| ) |
| set_seed(42) |
| model = model.cpu() |
| all_numels = 0 |
| for name, p in sorted(model.named_parameters()): |
| all_numels += p.numel() |
| with torch.no_grad(): |
| for name, p in sorted(model.named_parameters()): |
| torch.nn.init.normal_(p, 0, 0.2) |
| print(name, p.shape, f"{p.numel() / all_numels * 100: .4f}%") |
| model.save_pretrained(save_folder) |
| ``` |
|
|
| </details> |
|
|
| ### Printing the model: |
|
|
| <details><summary>Click to expand</summary> |
|
|
| ```text |
| Gemma4ForConditionalGeneration( |
| (model): Gemma4Model( |
| (language_model): Gemma4TextModel( |
| (embed_tokens): Gemma4TextScaledWordEmbedding(262144, 8, padding_idx=0) |
| (layers): ModuleList( |
| (0): Gemma4TextDecoderLayer( |
| (self_attn): Gemma4TextAttention( |
| (q_norm): Gemma4RMSNorm() |
| (k_norm): Gemma4RMSNorm() |
| (v_norm): Gemma4RMSNorm() |
| (k_proj): Linear(in_features=8, out_features=128, bias=False) |
| (q_proj): Linear(in_features=8, out_features=256, bias=False) |
| (v_proj): Linear(in_features=8, out_features=128, bias=False) |
| (o_proj): Linear(in_features=256, out_features=8, bias=False) |
| ) |
| (mlp): Gemma4TextMLP( |
| (gate_proj): Linear(in_features=8, out_features=64, bias=False) |
| (up_proj): Linear(in_features=8, out_features=64, bias=False) |
| (down_proj): Linear(in_features=64, out_features=8, bias=False) |
| (act_fn): GELUTanh() |
| ) |
| (input_layernorm): Gemma4RMSNorm() |
| (post_attention_layernorm): Gemma4RMSNorm() |
| (pre_feedforward_layernorm): Gemma4RMSNorm() |
| (post_feedforward_layernorm): Gemma4RMSNorm() |
| (act_fn): GELUTanh() |
| (per_layer_input_gate): Linear(in_features=8, out_features=2, bias=False) |
| (per_layer_projection): Linear(in_features=2, out_features=8, bias=False) |
| (post_per_layer_input_norm): Gemma4RMSNorm() |
| ) |
| (1): Gemma4TextDecoderLayer( |
| (self_attn): Gemma4TextAttention( |
| (q_norm): Gemma4RMSNorm() |
| (k_norm): Gemma4RMSNorm() |
| (v_norm): Gemma4RMSNorm() |
| (k_proj): Linear(in_features=8, out_features=256, bias=False) |
| (q_proj): Linear(in_features=8, out_features=512, bias=False) |
| (v_proj): Linear(in_features=8, out_features=256, bias=False) |
| (o_proj): Linear(in_features=512, out_features=8, bias=False) |
| ) |
| (mlp): Gemma4TextMLP( |
| (gate_proj): Linear(in_features=8, out_features=64, bias=False) |
| (up_proj): Linear(in_features=8, out_features=64, bias=False) |
| (down_proj): Linear(in_features=64, out_features=8, bias=False) |
| (act_fn): GELUTanh() |
| ) |
| (input_layernorm): Gemma4RMSNorm() |
| (post_attention_layernorm): Gemma4RMSNorm() |
| (pre_feedforward_layernorm): Gemma4RMSNorm() |
| (post_feedforward_layernorm): Gemma4RMSNorm() |
| (act_fn): GELUTanh() |
| (per_layer_input_gate): Linear(in_features=8, out_features=2, bias=False) |
| (per_layer_projection): Linear(in_features=2, out_features=8, bias=False) |
| (post_per_layer_input_norm): Gemma4RMSNorm() |
| ) |
| (2): Gemma4TextDecoderLayer( |
| (self_attn): Gemma4TextAttention( |
| (q_norm): Gemma4RMSNorm() |
| (k_norm): Gemma4RMSNorm() |
| (v_norm): Gemma4RMSNorm() |
| (k_proj): Linear(in_features=8, out_features=128, bias=False) |
| (q_proj): Linear(in_features=8, out_features=256, bias=False) |
| (v_proj): Linear(in_features=8, out_features=128, bias=False) |
| (o_proj): Linear(in_features=256, out_features=8, bias=False) |
| ) |
| (mlp): Gemma4TextMLP( |
| (gate_proj): Linear(in_features=8, out_features=64, bias=False) |
| (up_proj): Linear(in_features=8, out_features=64, bias=False) |
| (down_proj): Linear(in_features=64, out_features=8, bias=False) |
| (act_fn): GELUTanh() |
| ) |
| (input_layernorm): Gemma4RMSNorm() |
| (post_attention_layernorm): Gemma4RMSNorm() |
| (pre_feedforward_layernorm): Gemma4RMSNorm() |
| (post_feedforward_layernorm): Gemma4RMSNorm() |
| (act_fn): GELUTanh() |
| (per_layer_input_gate): Linear(in_features=8, out_features=2, bias=False) |
| (per_layer_projection): Linear(in_features=2, out_features=8, bias=False) |
| (post_per_layer_input_norm): Gemma4RMSNorm() |
| ) |
| (3): Gemma4TextDecoderLayer( |
| (self_attn): Gemma4TextAttention( |
| (q_norm): Gemma4RMSNorm() |
| (k_norm): Gemma4RMSNorm() |
| (v_norm): Gemma4RMSNorm() |
| (k_proj): Linear(in_features=8, out_features=256, bias=False) |
| (q_proj): Linear(in_features=8, out_features=512, bias=False) |
| (v_proj): Linear(in_features=8, out_features=256, bias=False) |
| (o_proj): Linear(in_features=512, out_features=8, bias=False) |
| ) |
| (mlp): Gemma4TextMLP( |
| (gate_proj): Linear(in_features=8, out_features=64, bias=False) |
| (up_proj): Linear(in_features=8, out_features=64, bias=False) |
| (down_proj): Linear(in_features=64, out_features=8, bias=False) |
| (act_fn): GELUTanh() |
| ) |
| (input_layernorm): Gemma4RMSNorm() |
| (post_attention_layernorm): Gemma4RMSNorm() |
| (pre_feedforward_layernorm): Gemma4RMSNorm() |
| (post_feedforward_layernorm): Gemma4RMSNorm() |
| (act_fn): GELUTanh() |
| (per_layer_input_gate): Linear(in_features=8, out_features=2, bias=False) |
| (per_layer_projection): Linear(in_features=2, out_features=8, bias=False) |
| (post_per_layer_input_norm): Gemma4RMSNorm() |
| ) |
| ) |
| (norm): Gemma4RMSNorm() |
| (rotary_emb): Gemma4TextRotaryEmbedding() |
| (embed_tokens_per_layer): Gemma4TextScaledWordEmbedding(262144, 8, padding_idx=0) |
| (per_layer_model_projection): Linear(in_features=8, out_features=8, bias=False) |
| (per_layer_projection_norm): Gemma4RMSNorm() |
| ) |
| (vision_tower): Gemma4VisionModel( |
| (patch_embedder): Gemma4VisionPatchEmbedder( |
| (input_proj): Linear(in_features=768, out_features=8, bias=False) |
| ) |
| (encoder): Gemma4VisionEncoder( |
| (rotary_emb): Gemma4VisionRotaryEmbedding() |
| (layers): ModuleList( |
| (0-1): 2 x Gemma4VisionEncoderLayer( |
| (self_attn): Gemma4VisionAttention( |
| (q_proj): Gemma4ClippableLinear( |
| (linear): Linear(in_features=8, out_features=128, bias=False) |
| ) |
| (k_proj): Gemma4ClippableLinear( |
| (linear): Linear(in_features=8, out_features=128, bias=False) |
| ) |
| (v_proj): Gemma4ClippableLinear( |
| (linear): Linear(in_features=8, out_features=128, bias=False) |
| ) |
| (o_proj): Gemma4ClippableLinear( |
| (linear): Linear(in_features=128, out_features=8, bias=False) |
| ) |
| (q_norm): Gemma4RMSNorm() |
| (k_norm): Gemma4RMSNorm() |
| (v_norm): Gemma4RMSNorm() |
| ) |
| (mlp): Gemma4VisionMLP( |
| (gate_proj): Gemma4ClippableLinear( |
| (linear): Linear(in_features=8, out_features=64, bias=False) |
| ) |
| (up_proj): Gemma4ClippableLinear( |
| (linear): Linear(in_features=8, out_features=64, bias=False) |
| ) |
| (down_proj): Gemma4ClippableLinear( |
| (linear): Linear(in_features=64, out_features=8, bias=False) |
| ) |
| (act_fn): GELUTanh() |
| ) |
| (input_layernorm): Gemma4RMSNorm() |
| (post_attention_layernorm): Gemma4RMSNorm() |
| (pre_feedforward_layernorm): Gemma4RMSNorm() |
| (post_feedforward_layernorm): Gemma4RMSNorm() |
| ) |
| ) |
| ) |
| (pooler): Gemma4VisionPooler() |
| ) |
| (embed_vision): Gemma4MultimodalEmbedder( |
| (embedding_projection): Linear(in_features=8, out_features=8, bias=False) |
| (embedding_pre_projection_norm): Gemma4RMSNorm() |
| ) |
| (audio_tower): Gemma4AudioModel( |
| (subsample_conv_projection): Gemma4AudioSubSampleConvProjection( |
| (layer0): Gemma4AudioSubSampleConvProjectionLayer( |
| (conv): Conv2d(1, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) |
| (norm): LayerNorm((128,), eps=1e-06, elementwise_affine=True) |
| (act): ReLU() |
| ) |
| (layer1): Gemma4AudioSubSampleConvProjectionLayer( |
| (conv): Conv2d(128, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) |
| (norm): LayerNorm((32,), eps=1e-06, elementwise_affine=True) |
| (act): ReLU() |
| ) |
| (input_proj_linear): Linear(in_features=1024, out_features=64, bias=False) |
| ) |
| (rel_pos_enc): Gemma4AudioRelPositionalEncoding() |
| (layers): ModuleList( |
| (0-1): 2 x Gemma4AudioLayer( |
| (feed_forward1): Gemma4AudioFeedForward( |
| (ffw_layer_1): Gemma4ClippableLinear( |
| (linear): Linear(in_features=64, out_features=256, bias=False) |
| ) |
| (ffw_layer_2): Gemma4ClippableLinear( |
| (linear): Linear(in_features=256, out_features=64, bias=False) |
| ) |
| (pre_layer_norm): Gemma4RMSNorm() |
| (post_layer_norm): Gemma4RMSNorm() |
| (act_fn): SiLUActivation() |
| ) |
| (feed_forward2): Gemma4AudioFeedForward( |
| (ffw_layer_1): Gemma4ClippableLinear( |
| (linear): Linear(in_features=64, out_features=256, bias=False) |
| ) |
| (ffw_layer_2): Gemma4ClippableLinear( |
| (linear): Linear(in_features=256, out_features=64, bias=False) |
| ) |
| (pre_layer_norm): Gemma4RMSNorm() |
| (post_layer_norm): Gemma4RMSNorm() |
| (act_fn): SiLUActivation() |
| ) |
| (self_attn): Gemma4AudioAttention( |
| (q_proj): Gemma4ClippableLinear( |
| (linear): Linear(in_features=64, out_features=64, bias=False) |
| ) |
| (k_proj): Gemma4ClippableLinear( |
| (linear): Linear(in_features=64, out_features=64, bias=False) |
| ) |
| (v_proj): Gemma4ClippableLinear( |
| (linear): Linear(in_features=64, out_features=64, bias=False) |
| ) |
| (post): Gemma4ClippableLinear( |
| (linear): Linear(in_features=64, out_features=64, bias=False) |
| ) |
| (relative_k_proj): Linear(in_features=64, out_features=64, bias=False) |
| ) |
| (lconv1d): Gemma4AudioLightConv1d( |
| (linear_start): Gemma4ClippableLinear( |
| (linear): Linear(in_features=64, out_features=128, bias=False) |
| ) |
| (linear_end): Gemma4ClippableLinear( |
| (linear): Linear(in_features=64, out_features=64, bias=False) |
| ) |
| (depthwise_conv1d): Gemma4AudioCausalConv1d(64, 64, kernel_size=(5,), stride=(1,), groups=64, bias=False) |
| (pre_layer_norm): Gemma4RMSNorm() |
| (conv_norm): Gemma4RMSNorm() |
| (act_fn): SiLUActivation() |
| ) |
| (norm_pre_attn): Gemma4RMSNorm() |
| (norm_post_attn): Gemma4RMSNorm() |
| (norm_out): Gemma4RMSNorm() |
| ) |
| ) |
| (output_proj): Linear(in_features=64, out_features=32, bias=True) |
| ) |
| (embed_audio): Gemma4MultimodalEmbedder( |
| (embedding_projection): Linear(in_features=32, out_features=8, bias=False) |
| (embedding_pre_projection_norm): Gemma4RMSNorm() |
| ) |
| ) |
| (lm_head): Linear(in_features=8, out_features=262144, bias=False) |
| ) |
| ``` |
|
|
| </details> |
