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--- |
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library_name: transformers |
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base_model: |
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- stepfun-ai/Step3-VL-10B |
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--- |
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This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from [stepfun-ai/Step3-VL-10B](https://huggingface.co/stepfun-ai/Step3-VL-10B). |
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| File path | Size | |
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|------|------| |
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| model.safetensors | 6.0MB | |
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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-vl \ |
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--trust-remote-code \ |
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--reasoning-parser deepseek_r1 \ |
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--enable-auto-tool-choice \ |
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--tool-call-parser hermes |
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``` |
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- Transformers |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoProcessor |
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model_id = "tiny-random/step3-vl" |
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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://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG" |
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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( |
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model_id, |
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trust_remote_code=True, |
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) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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torch_dtype=torch.bfloat16, |
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device_map="cuda", |
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trust_remote_code=True, |
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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_large_projector": "model.vit_large_projector", |
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} |
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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( |
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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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<details> |
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<summary>Python codes</summary> |
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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, list_repo_files |
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from safetensors.torch import save_file |
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from transformers import ( |
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AutoConfig, |
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AutoModel, |
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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-VL-10B" |
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save_folder = "/tmp/tiny-random/step3-vl" |
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Path(save_folder).mkdir(parents=True, exist_ok=True) |
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for f in list_repo_files(source_model_id, repo_type="model"): |
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if (f.endswith('.json') or f.endswith('.py') or f.endswith('.model') or f.endswith('.jinja')) and ( |
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not f.endswith('.index.json') |
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): |
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hf_hub_download(repo_id=source_model_id, filename=f, |
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repo_type="model", local_dir=save_folder) |
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def replace_file(filepath, old_string, new_string): |
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with open(filepath, 'r', encoding='utf-8') as f: |
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code = f.read() |
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code = code.replace(old_string, new_string) |
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with open(filepath, 'w', encoding='utf-8') as f: |
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f.write(code) |
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with open(f'{save_folder}/config.json') as f: |
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config_json = json.load(f) |
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config_json['text_config'].update({ |
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'num_hidden_layers': 2, |
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'hidden_size': 8, |
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'head_dim': 32, |
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'intermediate_size': 64, |
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'num_attention_heads': 8, |
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"num_key_value_heads": 4, |
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'tie_word_embeddings': False, |
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}) |
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config_json['vision_config'].update({ |
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'width': 64, |
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'layers': 2, |
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'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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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() |
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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) |
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model_new = torch.nn.Identity() |
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model_new.model = model.model.language_model |
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model_new.vision_model = model.model.vision_model |
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model_new.lm_head = model.lm_head |
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model_new.vit_large_projector = model.model.vit_large_projector |
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state_dict = model_new.state_dict() |
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save_file(state_dict, f"{save_folder}/model.safetensors") |
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``` |
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</details> |
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### Printing the model: |
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<details><summary>Click to expand</summary> |
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```text |
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Step3VL10BForCausalLM( |
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(model): StepRoboticsModel( |
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(vision_model): StepRoboticsVisionEncoder( |
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(conv1): Conv2d(3, 64, kernel_size=(14, 14), stride=(14, 14), bias=False) |
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(ln_pre): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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(ln_post): Identity() |
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(transformer): EncoderVisionTransformer( |
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(resblocks): ModuleList( |
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(0-1): 2 x EncoderVisionBlock( |
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(attn): EncoderVisionAttention( |
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(out_proj): Linear(in_features=64, out_features=64, bias=True) |
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(rope): EncoderRope2D() |
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) |
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(ln_1): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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(ln_2): LayerNorm((64,), eps=1e-05, elementwise_affine=True) |
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(mlp): EncoderMLP( |
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(c_fc): Linear(in_features=64, out_features=373, bias=True) |
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(act_fn): QuickGELUActivation() |
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(c_proj): Linear(in_features=373, out_features=64, bias=True) |
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) |
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(ls_1): EncoderLayerScale() |
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(ls_2): EncoderLayerScale() |
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) |
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) |
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) |
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(vit_downsampler1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) |
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(vit_downsampler2): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) |
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) |
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(language_model): Qwen3Model( |
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(embed_tokens): Embedding(151936, 8) |
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(layers): ModuleList( |
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(0-1): 2 x Qwen3DecoderLayer( |
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(self_attn): Qwen3Attention( |
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(q_proj): Linear(in_features=8, out_features=256, bias=False) |
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(k_proj): Linear(in_features=8, out_features=128, bias=False) |
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(v_proj): Linear(in_features=8, out_features=128, bias=False) |
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(o_proj): Linear(in_features=256, out_features=8, bias=False) |
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(q_norm): Qwen3RMSNorm((32,), eps=1e-06) |
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(k_norm): Qwen3RMSNorm((32,), eps=1e-06) |
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) |
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(mlp): Qwen3MLP( |
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(gate_proj): Linear(in_features=8, out_features=64, bias=False) |
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(up_proj): Linear(in_features=8, out_features=64, bias=False) |
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(down_proj): Linear(in_features=64, out_features=8, bias=False) |
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(act_fn): SiLUActivation() |
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) |
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(input_layernorm): Qwen3RMSNorm((8,), eps=1e-06) |
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(post_attention_layernorm): Qwen3RMSNorm((8,), eps=1e-06) |
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) |
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) |
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(norm): Qwen3RMSNorm((8,), eps=1e-06) |
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(rotary_emb): Qwen3RotaryEmbedding() |
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) |
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(vit_large_projector): Linear(in_features=256, out_features=8, bias=False) |
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) |
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(lm_head): Linear(in_features=8, out_features=151936, bias=False) |
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) |
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``` |
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</details> |
