---
library_name: transformers
base_model:
- google/gemma-4-31B-it-assistant
---
This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [google/gemma-4-31B-it-assistant](https://huggingface.co/google/gemma-4-31B-it-assistant).
| File path | Size |
|------|------|
| model.safetensors | 4.3MB |
### Example usage:
```python
import torch
from transformers import AutoModelForCausalLM, AutoProcessor, AutoModelForMultimodalLM
model_id = "tiny-random/gemma-4-assistant"
target_model_id = "tiny-random/gemma-4-moe"
processor = AutoProcessor.from_pretrained(target_model_id)
target_model = AutoModelForMultimodalLM.from_pretrained(
target_model_id,
dtype=torch.bfloat16,
device_map="auto",
)
assistant_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(target_model.device)
input_len = inputs["input_ids"].shape[-1]
print("input_len:", input_len)
outputs = target_model.generate(
**inputs,
assistant_model=assistant_model,
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:
Click to expand
```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,
Gemma4AssistantForCausalLM,
Gemma4ForConditionalGeneration,
GenerationConfig,
set_seed,
)
source_model_id = "google/gemma-4-31B-it-assistant"
save_folder = "/tmp/tiny-random/gemma-4-assistant"
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["backbone_hidden_size"] = 8
config_json["text_config"].update(
{
"global_head_dim": 64,
"head_dim": 32,
"hidden_size": 8,
"intermediate_size": 64,
"layer_types": [
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
],
"moe_intermediate_size": 32,
"num_attention_heads": 8,
"num_hidden_layers": 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 = Gemma4AssistantForCausalLM(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 = sum(p.numel() for p in model.parameters())
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:
Click to expand
```text
Gemma4AssistantForCausalLM(
(model): Gemma4TextModel(
(embed_tokens): Gemma4TextScaledWordEmbedding(262144, 8, padding_idx=0)
(layers): ModuleList(
(0-2): 3 x Gemma4TextDecoderLayer(
(self_attn): Gemma4TextAttention(
(q_proj): Linear(in_features=8, out_features=256, bias=False)
(q_norm): Gemma4RMSNorm()
(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()
(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()
)
(lm_head): Linear(in_features=8, out_features=262144, bias=False)
(pre_projection): Linear(in_features=16, out_features=8, bias=False)
(post_projection): Linear(in_features=8, out_features=8, bias=False)
)
```