Tiny dummy models
Collection
Randomly initialized tiny models for debugging/testing purpose
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56 items
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Updated
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3
This model is randomly initialized, using the config from state-spaces/mamba-2.8b-hf but with smaller size.
Codes:
import os
import torch
import transformers
from huggingface_hub import create_repo, upload_folder
source_model_id = 'state-spaces/mamba-2.8b-hf'
tiny_random_name = 'mamba-tiny-random'
save_path = f'/tmp/yujiepan/{tiny_random_name}'
repo_id = f'yujiepan/{tiny_random_name}'
config = transformers.AutoConfig.from_pretrained(
source_model_id, trust_remote_code=True)
config.hidden_size = 8
config.expand = 4
config.intermediate_size = 32
config.state_size = 8
config.num_hidden_layers = 2
config.n_layer = 2
config.torch_dtype = torch.bfloat16
model = transformers.AutoModelForCausalLM.from_config(
config, torch_dtype=torch.bfloat16,
trust_remote_code=True,
)
model.generation_config = transformers.GenerationConfig.from_pretrained(
source_model_id,
trust_remote_code=True,
)
transformers.set_seed(42)
with torch.no_grad():
for name, p in sorted(model.named_parameters()):
print(name, p.shape)
torch.nn.init.uniform_(p, -0.5, 0.5)
model.save_pretrained(save_path)
tokenizer = transformers.AutoTokenizer.from_pretrained(
source_model_id, trust_remote_code=True)
result = transformers.pipelines.pipeline(
'text-generation',
model=model, tokenizer=tokenizer,
device='cuda',
max_new_tokens=16,
)('Hello')
print(result)
model.save_pretrained(save_path)
tokenizer.save_pretrained(save_path)
os.system(f'ls -alh {save_path}')
create_repo(repo_id, exist_ok=True)
upload_folder(repo_id=repo_id, folder_path=save_path)