This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from google/gemma-4-31B-it.
| File path | Size |
|---|---|
| model.safetensors | 4.6MB |
Example usage:
import torch
from transformers import AutoModelForCausalLM, AutoProcessor
model_id = "tiny-random/gemma-4-dense"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id, dtype=torch.bfloat16, device_map="auto"
)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG",
},
{"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("<|image|>", "I")
response = response.replace("<|video|>", "V")
print(response)
Codes to create this repo:
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-4-31B-it"
save_folder = "/tmp/tiny-random/gemma-4-dense"
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["text_config"].update(
{
"global_head_dim": 64,
"head_dim": 32,
"hidden_size": 8,
# "hidden_size_per_layer_input": 0, # only "E" variants have this
"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": 0, # only "E" variants have this
}
)
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)
Printing the model:
Gemma4ForConditionalGeneration(
(model): Gemma4Model(
(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()
)
(language_model): Gemma4TextModel(
(embed_tokens): Gemma4TextScaledWordEmbedding(262144, 8, padding_idx=0)
(layers): ModuleList(
(0): Gemma4TextDecoderLayer(
(self_attn): Gemma4TextAttention(
(q_proj): Linear(in_features=8, out_features=256, bias=False)
(q_norm): Gemma4RMSNorm()
(k_norm): Gemma4RMSNorm()
(v_norm): Gemma4RMSNorm()
(k_proj): Linear(in_features=8, out_features=128, 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()
)
(1): Gemma4TextDecoderLayer(
(self_attn): Gemma4TextAttention(
(q_proj): Linear(in_features=8, out_features=512, bias=False)
(q_norm): Gemma4RMSNorm()
(k_norm): Gemma4RMSNorm()
(v_norm): Gemma4RMSNorm()
(k_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()
)
(2): Gemma4TextDecoderLayer(
(self_attn): Gemma4TextAttention(
(q_proj): Linear(in_features=8, out_features=256, bias=False)
(q_norm): Gemma4RMSNorm()
(k_norm): Gemma4RMSNorm()
(v_norm): Gemma4RMSNorm()
(k_proj): Linear(in_features=8, out_features=128, 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()
)
(3): Gemma4TextDecoderLayer(
(self_attn): Gemma4TextAttention(
(q_proj): Linear(in_features=8, out_features=512, bias=False)
(q_norm): Gemma4RMSNorm()
(k_norm): Gemma4RMSNorm()
(v_norm): Gemma4RMSNorm()
(k_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()
)
)
(norm): Gemma4RMSNorm()
(rotary_emb): Gemma4TextRotaryEmbedding()
)
(embed_vision): Gemma4MultimodalEmbedder(
(embedding_projection): Linear(in_features=8, out_features=8, bias=False)
(embedding_pre_projection_norm): Gemma4RMSNorm()
)
)
(lm_head): Linear(in_features=8, out_features=262144, bias=False)
)
Test environment:
- torch: 2.10.0+cu130
- transformers: 5.9.0