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--- |
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library_name: transformers |
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pipeline_tag: image-text-to-text |
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inference: true |
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widget: |
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- text: Hello! |
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example_title: Hello world |
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group: Python |
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base_model: |
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- zai-org/GLM-4.5V |
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--- |
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This tiny model is for debugging. It is randomly initialized with the config adapted from [zai-org/GLM-4.5V](https://huggingface.co/zai-org/GLM-4.5V). |
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### Example usage: |
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```python |
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import torch |
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from transformers import AutoProcessor, Glm4vMoeForConditionalGeneration |
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model_id = "tiny-random/glm-4.5v" |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "image", |
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"url": "https://upload.wikimedia.org/wikipedia/commons/f/fa/Grayscale_8bits_palette_sample_image.png" |
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}, |
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{ |
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"type": "text", |
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"text": "describe this image" |
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} |
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], |
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} |
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] |
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processor = AutoProcessor.from_pretrained(model_id) |
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model = Glm4vMoeForConditionalGeneration.from_pretrained( |
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model_id, |
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torch_dtype=torch.bfloat16, |
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device_map="auto", |
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) |
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inputs = processor.apply_chat_template( |
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messages, |
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tokenize=True, |
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add_generation_prompt=True, |
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return_dict=True, |
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return_tensors="pt" |
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).to(model.device) |
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inputs.pop("token_type_ids", None) |
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generated_ids = model.generate(**inputs, max_new_tokens=16) |
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output_text = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False) |
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print(output_text) |
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``` |
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### Codes to create this repo: |
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```python |
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import json |
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from pathlib import Path |
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import accelerate |
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import torch |
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from huggingface_hub import file_exists, hf_hub_download |
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from transformers import ( |
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AutoConfig, |
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AutoModelForCausalLM, |
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AutoProcessor, |
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GenerationConfig, |
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Glm4vForConditionalGeneration, |
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Glm4vMoeForConditionalGeneration, |
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set_seed, |
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) |
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from transformers.models.glm4v_moe.modeling_glm4v_moe import Glm4vMoeTextTopkRouter |
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source_model_id = "zai-org/GLM-4.5V" |
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save_folder = "/tmp/tiny-random/glm-4.5v" |
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processor = AutoProcessor.from_pretrained(source_model_id, trust_remote_code=True) |
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processor.save_pretrained(save_folder) |
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with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f: |
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config_json = json.load(f) |
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config_json['text_config'].update({ |
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"hidden_size": 32, |
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"head_dim": 32, |
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"intermediate_size": 128, |
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"first_k_dense_replace": 1, |
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"moe_intermediate_size": 64, |
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"num_attention_heads": 2, |
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"num_key_value_heads": 1, |
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"num_hidden_layers": 2, # one dense, one moe |
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"tie_word_embeddings": True, |
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}) |
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config_json['text_config']['rope_scaling']['mrope_section'] = [2, 2, 4] |
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config_json['vision_config']['hidden_size'] = 64 |
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config_json['vision_config']['depth'] = 2 |
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config_json['vision_config']['num_heads'] = 2 |
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config_json['vision_config']['intermediate_size'] = 128 |
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config_json['vision_config']['out_hidden_size'] = config_json['text_config']['hidden_size'] |
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with open(f"{save_folder}/config.json", "w", encoding='utf-8') as f: |
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json.dump(config_json, f, indent=2) |
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config = AutoConfig.from_pretrained( |
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save_folder, |
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trust_remote_code=True, |
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) |
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print(config) |
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torch.set_default_dtype(torch.bfloat16) |
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model = Glm4vMoeForConditionalGeneration(config) |
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torch.set_default_dtype(torch.float32) |
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if file_exists(filename="generation_config.json", repo_id=source_model_id, repo_type='model'): |
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model.generation_config = GenerationConfig.from_pretrained( |
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source_model_id, trust_remote_code=True, |
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) |
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set_seed(42) |
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model = model.cpu() # cpu is more stable for random initialization across machines |
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num_params = sum(p.numel() for p in model.parameters()) |
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with torch.no_grad(): |
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for name, p in sorted(model.named_parameters()): |
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torch.nn.init.normal_(p, 0, 0.1) |
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print(name, p.shape, p.dtype, p.device, f'{p.numel() / num_params * 100: .2f}%') |
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for _, m in sorted(model.named_modules()): |
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if isinstance(m, Glm4vMoeTextTopkRouter): |
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assert 'e_score_correction_bias' in m.state_dict() |
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torch.nn.init.normal_(m.e_score_correction_bias, 0, 1) |
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model.save_pretrained(save_folder) |
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print(model) |
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``` |
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### Printing the model: |
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```text |
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Glm4vMoeForConditionalGeneration( |
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(model): Glm4vMoeModel( |
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(visual): Glm4vMoeVisionModel( |
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(embeddings): Glm4vMoeVisionEmbeddings( |
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(position_embedding): Embedding(576, 64) |
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) |
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(patch_embed): Glm4vMoeVisionPatchEmbed( |
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(proj): Conv3d(3, 64, kernel_size=(2, 14, 14), stride=(2, 14, 14)) |
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) |
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(rotary_pos_emb): Glm4vMoeVisionRotaryEmbedding() |
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(blocks): ModuleList( |
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(0-1): 2 x Glm4vMoeVisionBlock( |
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(norm1): Glm4vMoeRMSNorm((64,), eps=1e-05) |
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(norm2): Glm4vMoeRMSNorm((64,), eps=1e-05) |
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(attn): Glm4vMoeVisionAttention( |
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(qkv): Linear(in_features=64, out_features=192, bias=False) |
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(proj): Linear(in_features=64, out_features=64, bias=False) |
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) |
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(mlp): Glm4vMoeisionMlp( |
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(gate_proj): Linear(in_features=64, out_features=32, bias=False) |
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(up_proj): Linear(in_features=64, out_features=32, bias=False) |
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(down_proj): Linear(in_features=32, out_features=64, bias=False) |
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(act_fn): SiLU() |
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) |
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) |
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) |
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(merger): Glm4vMoeVisionPatchMerger( |
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(proj): Linear(in_features=32, out_features=32, bias=False) |
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(post_projection_norm): LayerNorm((32,), eps=1e-05, elementwise_affine=True) |
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(gate_proj): Linear(in_features=32, out_features=128, bias=False) |
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(up_proj): Linear(in_features=32, out_features=128, bias=False) |
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(down_proj): Linear(in_features=128, out_features=32, bias=False) |
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(act1): GELU(approximate='none') |
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(act_fn): SiLU() |
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) |
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(post_conv_layernorm): Glm4vMoeRMSNorm((64,), eps=1e-05) |
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(downsample): Conv2d(64, 32, kernel_size=(2, 2), stride=(2, 2)) |
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(post_layernorm): Glm4vMoeRMSNorm((64,), eps=1e-05) |
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) |
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(language_model): Glm4vMoeTextModel( |
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(embed_tokens): Embedding(151552, 32, padding_idx=151329) |
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(layers): ModuleList( |
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(0): Glm4vMoeTextDecoderLayer( |
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(self_attn): Glm4vMoeTextAttention( |
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(q_proj): Linear(in_features=32, out_features=64, bias=True) |
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(k_proj): Linear(in_features=32, out_features=32, bias=True) |
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(v_proj): Linear(in_features=32, out_features=32, bias=True) |
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(o_proj): Linear(in_features=64, out_features=32, bias=False) |
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) |
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(mlp): Glm4vMoeTextMLP( |
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(gate_proj): Linear(in_features=32, out_features=128, bias=False) |
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(up_proj): Linear(in_features=32, out_features=128, bias=False) |
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(down_proj): Linear(in_features=128, out_features=32, bias=False) |
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(act_fn): SiLU() |
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) |
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(input_layernorm): Glm4vMoeTextRMSNorm((32,), eps=1e-05) |
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(post_attention_layernorm): Glm4vMoeTextRMSNorm((32,), eps=1e-05) |
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) |
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(1): Glm4vMoeTextDecoderLayer( |
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(self_attn): Glm4vMoeTextAttention( |
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(q_proj): Linear(in_features=32, out_features=64, bias=True) |
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(k_proj): Linear(in_features=32, out_features=32, bias=True) |
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(v_proj): Linear(in_features=32, out_features=32, bias=True) |
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(o_proj): Linear(in_features=64, out_features=32, bias=False) |
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) |
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(mlp): Glm4vMoeTextMoE( |
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(experts): ModuleList( |
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(0-127): 128 x Glm4vMoeTextMLP( |
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(gate_proj): Linear(in_features=32, out_features=64, bias=False) |
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(up_proj): Linear(in_features=32, out_features=64, bias=False) |
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(down_proj): Linear(in_features=64, out_features=32, bias=False) |
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(act_fn): SiLU() |
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) |
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) |
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(gate): Glm4vMoeTextTopkRouter() |
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(shared_experts): Glm4vMoeTextMLP( |
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(gate_proj): Linear(in_features=32, out_features=64, bias=False) |
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(up_proj): Linear(in_features=32, out_features=64, bias=False) |
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(down_proj): Linear(in_features=64, out_features=32, bias=False) |
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(act_fn): SiLU() |
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) |
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) |
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(input_layernorm): Glm4vMoeTextRMSNorm((32,), eps=1e-05) |
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(post_attention_layernorm): Glm4vMoeTextRMSNorm((32,), eps=1e-05) |
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) |
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) |
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(norm): Glm4vMoeRMSNorm((32,), eps=1e-05) |
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(rotary_emb): Glm4vMoeTextRotaryEmbedding() |
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) |
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) |
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(lm_head): Linear(in_features=32, out_features=151552, bias=False) |
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) |
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``` |
