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--- |
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library_name: transformers |
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pipeline_tag: text-generation |
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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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- stepfun-ai/step3 |
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--- |
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This tiny model is for debugging. It is randomly initialized with the config adapted from [stepfun-ai/step3](https://huggingface.co/stepfun-ai/step3). |
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Note: if you want the model version that follows transformers' naming, see the model without "-vllm" suffix. |
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### Example usage: |
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- vLLM |
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```bash |
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vllm serve tiny-random/step3-vllm --trust-remote-code |
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``` |
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- Transformers |
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```python |
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# ⚠️: it's more convenient to use the model without "-vllm" suffix, |
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# which follows transformers' naming. Here "key_mapping" is a workaround. |
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import torch |
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from transformers import AutoModelForCausalLM, AutoProcessor |
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model_id = "tiny-random/step3-vllm" |
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key_mapping = { |
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"^vision_model": "model.vision_model", |
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r"^model(?!\.(language_model|vision_model))": "model.language_model", |
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"vit_downsampler": "model.vit_downsampler", |
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"vit_downsampler2": "model.vit_downsampler2", |
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"vit_large_projector": "model.vit_large_projector", |
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} |
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processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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device_map="cuda", torch_dtype=torch.bfloat16, |
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trust_remote_code=True, key_mapping=key_mapping, |
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) |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"}, |
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{"type": "text", "text": "What's in this picture?"} |
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] |
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}, |
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] |
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inputs = processor.apply_chat_template( |
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messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt" |
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).to(model.device) |
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generate_ids = model.generate(**inputs, max_new_tokens=32, do_sample=False) |
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decoded = processor.decode(generate_ids[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=False) |
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print(decoded) |
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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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AutoTokenizer, |
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GenerationConfig, |
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set_seed, |
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) |
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source_model_id = "stepfun-ai/step3" |
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save_folder = "/tmp/tiny-random/step3-vllm" |
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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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def rewrite_automap(filepath: str, source_model_id: str, overrides: dict = None): |
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import json |
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with open(filepath, 'r', encoding='utf-8') as f: |
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config = json.load(f) |
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for k, v in config['auto_map'].items(): |
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v = v.split('--')[-1] |
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config['auto_map'][k] = f'{source_model_id}--{v}' |
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if overrides is not None: |
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config.update(overrides) |
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with open(filepath, 'w', encoding='utf - 8') as f: |
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json.dump(config, f, indent=2) |
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rewrite_automap(f'{save_folder}/processor_config.json', source_model_id) |
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rewrite_automap(f'{save_folder}/tokenizer_config.json', source_model_id) |
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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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for k, v in config_json['auto_map'].items(): |
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config_json['auto_map'][k] = f'{source_model_id}--{v}' |
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config_json['architectures'] = ["Step3VLForConditionalGeneration"] |
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config_json['text_config'].update({ |
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"hidden_size": 32, |
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"intermediate_size": 64, |
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"num_hidden_layers": 2, |
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"num_attention_heads": 2, |
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"num_attention_groups": 1, |
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"head_dim": 256, |
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"share_q_dim": 512, |
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"moe_layers_enum": "1", |
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"moe_num_experts": 8, |
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"moe_top_k": 3, |
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"moe_intermediate_size": 64, |
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"share_expert_dim": 64, |
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"tie_word_embeddings": True, |
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}) |
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config_json['vision_config'].update({ |
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"hidden_size": 64, |
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"output_hidden_size": 64, |
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"intermediate_size": 128, |
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"num_hidden_layers": 2, |
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"num_attention_heads": 2 |
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}) |
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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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automap = config_json['auto_map'] |
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torch.set_default_dtype(torch.bfloat16) |
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model = AutoModelForCausalLM.from_config(config, trust_remote_code=True) |
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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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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, 0.2) |
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print(name, p.shape) |
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model.save_pretrained(save_folder) |
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import safetensors |
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new_tensors = {} |
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with safetensors.safe_open(f'{save_folder}/model.safetensors', framework='pt', device='cpu') as f: |
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for k in list(f.keys()): |
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v = f.get_tensor(k) |
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if k.startswith('model.language_model.'): |
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k = k.replace('model.language_model.', 'model.') |
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new_tensors[k] = v |
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elif k.startswith('model.vi'): |
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k = k.replace('model.vi', 'vi') |
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new_tensors[k] = v |
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else: |
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new_tensors[k] = v |
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safetensors.torch.save_file(new_tensors, f"{save_folder}/model.safetensors") |
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rewrite_automap( |
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f'{save_folder}/config.json', source_model_id, |
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overrides=dict(architectures=['Step3VLForConditionalGeneration']), |
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) |
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for python_file in Path(save_folder).glob('*.py'): |
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if python_file.name.startswith('modeling_') or python_file.name.startswith('configuration_') or python_file.name.endswith('.py'): |
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python_file.unlink() |
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``` |
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### Printing the model: |
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```text |
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Step3vForConditionalGeneration( |
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(model): Step3vModel( |
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(vision_model): StepCLIPVisionTransformer( |
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(embeddings): StepCLIPVisionEmbeddings( |
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(patch_embedding): Conv2d(3, 64, kernel_size=(14, 14), stride=(14, 14)) |
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(position_embedding): Embedding(2705, 64) |
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) |
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(transformer): StepCLIPEncoder( |
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(layers): ModuleList( |
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(0-1): 2 x StepCLIPEncoderLayer( |
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(layer_norm1): LayerNorm((64,), eps=1e-06, elementwise_affine=True) |
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(layer_norm2): LayerNorm((64,), eps=1e-06, elementwise_affine=True) |
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(self_attn): StepCLIPAttention( |
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(qkv_proj): Linear(in_features=64, out_features=192, bias=True) |
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(out_proj): Linear(in_features=64, out_features=64, bias=True) |
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) |
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(mlp): StepCLIPMLP( |
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(fc1): Linear(in_features=64, out_features=128, bias=True) |
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(act): QuickGELUActivation() |
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(fc2): Linear(in_features=128, out_features=64, bias=True) |
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) |
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) |
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) |
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) |
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) |
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(language_model): Step3Model( |
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(embed_tokens): Embedding(128815, 32) |
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(layers): ModuleList( |
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(0): Step3vDecoderLayer( |
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(self_attn): Step3vAttention( |
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(q_proj): Linear(in_features=32, out_features=512, bias=False) |
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(k_proj): Linear(in_features=32, out_features=256, bias=False) |
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(v_proj): Linear(in_features=32, out_features=256, bias=False) |
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(o_proj): Linear(in_features=512, out_features=32, bias=False) |
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(inter_norm): Step3vRMSNorm((512,), eps=1e-05) |
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(wq): Linear(in_features=512, out_features=512, bias=False) |
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) |
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(mlp): Step3vMLP( |
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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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(input_layernorm): Step3vRMSNorm((32,), eps=1e-05) |
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(post_attention_layernorm): Step3vRMSNorm((32,), eps=1e-05) |
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) |
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(1): Step3vDecoderLayer( |
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(self_attn): Step3vAttention( |
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(q_proj): Linear(in_features=32, out_features=512, bias=False) |
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(k_proj): Linear(in_features=32, out_features=256, bias=False) |
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(v_proj): Linear(in_features=32, out_features=256, bias=False) |
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(o_proj): Linear(in_features=512, out_features=32, bias=False) |
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(inter_norm): Step3vRMSNorm((512,), eps=1e-05) |
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(wq): Linear(in_features=512, out_features=512, bias=False) |
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) |
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(moe): Step3vMoEMLP( |
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(gate): Linear(in_features=32, out_features=8, bias=False) |
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(up_proj): MoELinear() |
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(gate_proj): MoELinear() |
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(down_proj): MoELinear() |
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(act_fn): SiLU() |
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) |
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(share_expert): Step3vMLP( |
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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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(input_layernorm): Step3vRMSNorm((32,), eps=1e-05) |
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(post_attention_layernorm): Step3vRMSNorm((32,), eps=1e-05) |
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) |
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) |
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(norm): Step3vRMSNorm((32,), eps=1e-05) |
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(rotary_emb): Step3vRotaryEmbedding() |
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) |
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(vit_downsampler): Conv2d(64, 64, kernel_size=(2, 2), stride=(2, 2)) |
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(vit_downsampler2): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) |
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(vit_large_projector): Linear(in_features=128, out_features=32, bias=False) |
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) |
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(lm_head): Linear(in_features=32, out_features=128815, bias=False) |
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) |
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``` |
