metadata
library_name: transformers
base_model:
- stepfun-ai/Step3-VL-10B
This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from stepfun-ai/Step3-VL-10B.
| File path | Size |
|---|---|
| model.safetensors | 6.0MB |
Example usage:
- vLLM
vllm serve tiny-random/step3-vl \
--trust-remote-code \
--reasoning-parser deepseek_r1 \
--enable-auto-tool-choice \
--tool-call-parser hermes
- Transformers
import torch
from transformers import AutoModelForCausalLM, AutoProcessor
model_id = "tiny-random/step3-vl"
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"
},
{
"type": "text",
"text": "describe this image"
}
],
}
]
processor = AutoProcessor.from_pretrained(
model_id,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="cuda",
trust_remote_code=True,
key_mapping={
"^vision_model": "model.vision_model",
r"^model(?!\.(language_model|vision_model))": "model.language_model",
"vit_large_projector": "model.vit_large_projector",
}
)
inputs = processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt"
).to(model.device)
inputs.pop("token_type_ids", None)
generated_ids = model.generate(**inputs, max_new_tokens=16)
output_text = processor.decode(
generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
print(output_text)
Codes to create this repo:
Python codes
import json
from pathlib import Path
import accelerate
import torch
from huggingface_hub import file_exists, hf_hub_download, list_repo_files
from safetensors.torch import save_file
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoProcessor,
AutoTokenizer,
GenerationConfig,
set_seed,
)
source_model_id = "stepfun-ai/Step3-VL-10B"
save_folder = "/tmp/tiny-random/step3-vl"
Path(save_folder).mkdir(parents=True, exist_ok=True)
for f in list_repo_files(source_model_id, repo_type="model"):
if (f.endswith('.json') or f.endswith('.py') or f.endswith('.model') or f.endswith('.jinja')) and (
not f.endswith('.index.json')
):
hf_hub_download(repo_id=source_model_id, filename=f,
repo_type="model", local_dir=save_folder)
def replace_file(filepath, old_string, new_string):
with open(filepath, 'r', encoding='utf-8') as f:
code = f.read()
code = code.replace(old_string, new_string)
with open(filepath, 'w', encoding='utf-8') as f:
f.write(code)
with open(f'{save_folder}/config.json') as f:
config_json = json.load(f)
config_json['text_config'].update({
'num_hidden_layers': 2,
'hidden_size': 8,
'head_dim': 32,
'intermediate_size': 64,
'num_attention_heads': 8,
"num_key_value_heads": 4,
'tie_word_embeddings': False,
})
config_json['vision_config'].update({
'width': 64,
'layers': 2,
'heads': 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 = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
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()
with torch.no_grad():
for name, p in sorted(model.named_parameters()):
torch.nn.init.normal_(p, 0, 0.1)
print(name, p.shape)
model_new = torch.nn.Identity()
model_new.model = model.model.language_model
model_new.vision_model = model.model.vision_model
model_new.lm_head = model.lm_head
model_new.vit_large_projector = model.model.vit_large_projector
state_dict = model_new.state_dict()
save_file(state_dict, f"{save_folder}/model.safetensors")
Printing the model:
Click to expand
Step3VL10BForCausalLM(
(model): StepRoboticsModel(
(vision_model): StepRoboticsVisionEncoder(
(conv1): Conv2d(3, 64, kernel_size=(14, 14), stride=(14, 14), bias=False)
(ln_pre): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(ln_post): Identity()
(transformer): EncoderVisionTransformer(
(resblocks): ModuleList(
(0-1): 2 x EncoderVisionBlock(
(attn): EncoderVisionAttention(
(out_proj): Linear(in_features=64, out_features=64, bias=True)
(rope): EncoderRope2D()
)
(ln_1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(ln_2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(mlp): EncoderMLP(
(c_fc): Linear(in_features=64, out_features=373, bias=True)
(act_fn): QuickGELUActivation()
(c_proj): Linear(in_features=373, out_features=64, bias=True)
)
(ls_1): EncoderLayerScale()
(ls_2): EncoderLayerScale()
)
)
)
(vit_downsampler1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
(vit_downsampler2): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
)
(language_model): Qwen3Model(
(embed_tokens): Embedding(151936, 8)
(layers): ModuleList(
(0-1): 2 x Qwen3DecoderLayer(
(self_attn): Qwen3Attention(
(q_proj): Linear(in_features=8, out_features=256, bias=False)
(k_proj): Linear(in_features=8, out_features=128, bias=False)
(v_proj): Linear(in_features=8, out_features=128, bias=False)
(o_proj): Linear(in_features=256, out_features=8, bias=False)
(q_norm): Qwen3RMSNorm((32,), eps=1e-06)
(k_norm): Qwen3RMSNorm((32,), eps=1e-06)
)
(mlp): Qwen3MLP(
(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): Qwen3RMSNorm((8,), eps=1e-06)
(post_attention_layernorm): Qwen3RMSNorm((8,), eps=1e-06)
)
)
(norm): Qwen3RMSNorm((8,), eps=1e-06)
(rotary_emb): Qwen3RotaryEmbedding()
)
(vit_large_projector): Linear(in_features=256, out_features=8, bias=False)
)
(lm_head): Linear(in_features=8, out_features=151936, bias=False)
)