---
library_name: transformers
base_model:
- openbmb/MiniCPM-V-4.6
---
This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [openbmb/MiniCPM-V-4.6](https://huggingface.co/openbmb/MiniCPM-V-4.6).
| File path | Size |
|------|------|
| model.safetensors | 7.2MB |
### Example usage:
```python
import torch
from transformers import MiniCPMV4_6ForConditionalGeneration, AutoProcessor
model_id = "tiny-random/minicpm-v-4.6"
model = MiniCPMV4_6ForConditionalGeneration.from_pretrained(
model_id,
trust_remote_code=True,
attn_implementation='sdpa',
dtype=torch.bfloat16,
)
model = model.eval().cuda()
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
messages = [
{
"role": "user",
"content": [
{"type": "video", "url": "https://huggingface.co/datasets/openbmb/DemoCase/resolve/main/football.mp4"},
{"type": "text", "text": "Describe this video in detail. Follow the timeline and focus on on-screen text, interface changes, main actions, and scene changes."},
],
},
{
"role": "assistant",
"content": [{"type": "text", "text": "Dummy response for video"}],
},
{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/datasets/openbmb/DemoCase/resolve/main/refract.png"},
{"type": "text", "text": "What causes this phenomenon?"},
],
},
]
downsample_mode = "16x" # Using `downsample_mode="4x"` for Finer Detail
inputs = processor.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True,
return_dict=True, return_tensors="pt",
downsample_mode=downsample_mode,
max_num_frames=128,
stack_frames=1,
max_slice_nums=1,
use_image_id=False,
).to(model.device)
generated_ids = model.generate(**inputs, downsample_mode=downsample_mode, max_new_tokens=32)[0]
output_text = processor.decode(generated_ids, skip_special_tokens=False, clean_up_tokenization_spaces=False)
print(output_text.replace('<|video_pad|>', 'V').replace('<|image_pad|>', 'I'))
```
### Codes to create this repo:
Click to expand
```python
import json
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from transformers import (
AutoConfig,
AutoModel,
AutoProcessor,
AutoTokenizer,
GenerationConfig,
set_seed,
)
source_model_id = "openbmb/MiniCPM-V-4.6"
save_folder = "/tmp/tiny-random/minicpm-v-46"
processor = AutoProcessor.from_pretrained(source_model_id, trust_remote_code=True)
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: dict = json.load(f)
text_config = config_json["text_config"]
text_config.update({
"head_dim": 32,
"hidden_size": 8,
"intermediate_size": 64,
"layer_types": [
"linear_attention",
"linear_attention",
"linear_attention",
"full_attention",
],
"linear_key_head_dim": 32,
"linear_num_key_heads": 4,
"linear_num_value_heads": 4,
"linear_value_head_dim": 32,
"num_attention_heads": 8,
"num_hidden_layers": 4,
"num_key_value_heads": 2,
})
vision_config = config_json["vision_config"]
vision_config.update({
"hidden_size": 128,
"intermediate_size": 128,
"num_attention_heads": 4,
"num_hidden_layers": 2,
})
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 = AutoModel.from_config(config, trust_remote_code=True)
torch.set_default_dtype(torch.float32)
model.generation_config = GenerationConfig.from_pretrained(
source_model_id, trust_remote_code=True,
)
set_seed(42)
num_params = 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, p.dtype, p.device, f'{p.numel() / num_params * 100: .2f}%')
model.save_pretrained(save_folder)
for f in Path(save_folder).glob('*.py'):
f.unlink()
```
### Printing the model:
Click to expand
```text
MiniCPMV4_6Model(
(vision_tower): MiniCPMV4_6VisionModel(
(embeddings): MiniCPMV4_6VisionEmbeddings(
(patch_embedding): Conv2d(3, 128, kernel_size=(14, 14), stride=(14, 14), padding=valid)
(position_embedding): Embedding(4900, 128)
)
(encoder): MiniCPMV4_6VisionEncoder(
(layers): ModuleList(
(0-1): 2 x MiniCPMV4_6VisionEncoderLayer(
(layer_norm1): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
(self_attn): MiniCPMV4_6VisionAttention(
(k_proj): Linear(in_features=128, out_features=128, bias=True)
(v_proj): Linear(in_features=128, out_features=128, bias=True)
(q_proj): Linear(in_features=128, out_features=128, bias=True)
(out_proj): Linear(in_features=128, out_features=128, bias=True)
)
(layer_norm2): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
(mlp): MiniCPMV4_6VisionMLP(
(activation_fn): GELUTanh()
(fc1): Linear(in_features=128, out_features=128, bias=True)
(fc2): Linear(in_features=128, out_features=128, bias=True)
)
)
)
)
(post_layernorm): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
(vit_merger): MiniCPMV4_6ViTWindowAttentionMerger(
(self_attn): MiniCPMV4_6VisionAttention(
(k_proj): Linear(in_features=128, out_features=128, bias=True)
(v_proj): Linear(in_features=128, out_features=128, bias=True)
(q_proj): Linear(in_features=128, out_features=128, bias=True)
(out_proj): Linear(in_features=128, out_features=128, bias=True)
)
(layer_norm1): LayerNorm((128,), eps=1e-06, elementwise_affine=True)
(pre_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
(linear_1): Linear(in_features=512, out_features=512, bias=True)
(act): GELUTanh()
(linear_2): Linear(in_features=512, out_features=128, bias=True)
)
)
(language_model): Qwen3_5TextModel(
(embed_tokens): Embedding(248094, 8)
(layers): ModuleList(
(0-2): 3 x Qwen3_5DecoderLayer(
(linear_attn): Qwen3_5GatedDeltaNet(
(act): SiLUActivation()
(conv1d): Conv1d(384, 384, kernel_size=(4,), stride=(1,), padding=(3,), groups=384, bias=False)
(norm): Qwen3_5RMSNormGated()
(out_proj): Linear(in_features=128, out_features=8, bias=False)
(in_proj_qkv): Linear(in_features=8, out_features=384, bias=False)
(in_proj_z): Linear(in_features=8, out_features=128, bias=False)
(in_proj_b): Linear(in_features=8, out_features=4, bias=False)
(in_proj_a): Linear(in_features=8, out_features=4, bias=False)
)
(mlp): Qwen3_5MLP(
(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): SiLUActivation()
)
(input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
(post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
)
(3): Qwen3_5DecoderLayer(
(self_attn): Qwen3_5Attention(
(q_proj): Linear(in_features=8, out_features=512, bias=False)
(k_proj): Linear(in_features=8, out_features=64, bias=False)
(v_proj): Linear(in_features=8, out_features=64, bias=False)
(o_proj): Linear(in_features=256, out_features=8, bias=False)
(q_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
(k_norm): Qwen3_5RMSNorm((32,), eps=1e-06)
)
(mlp): Qwen3_5MLP(
(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): SiLUActivation()
)
(input_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
(post_attention_layernorm): Qwen3_5RMSNorm((8,), eps=1e-06)
)
)
(norm): Qwen3_5RMSNorm((8,), eps=1e-06)
(rotary_emb): Qwen3_5TextRotaryEmbedding()
)
(merger): MiniCPMV4_6Merger(
(mlp): ModuleList(
(0): MiniCPMV4_6DownsampleMLP(
(pre_norm): LayerNorm((512,), eps=1e-06, elementwise_affine=True)
(linear_1): Linear(in_features=512, out_features=512, bias=True)
(act): GELU(approximate='none')
(linear_2): Linear(in_features=512, out_features=8, bias=True)
)
)
)
)
```