Random weights generated using script derived from
yujiepan/deepseek-v3-tiny-random.
import os
from pathlib import Path
import torch
import transformers
from huggingface_hub import create_repo, upload_folder
from transformers import (AutoConfig, AutoModelForCausalLM, AutoTokenizer,
GenerationConfig, enable_full_determinism, pipeline,
set_seed)
model_id = "deepseek-ai/DeepSeek-V3"
repo_id = "modularai/deepseek-v3-small-random"
save_path = f"/home/ubuntu/mock-models/{repo_id}"
deepseek_config = AutoConfig.from_pretrained("deepseek-ai/DeepSeek-V3")
config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
config.num_hidden_layers = 2
config.first_k_dense_replace = 1
# transformers has not supported the customized quantization config
del config.quantization_config
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
tokenizer.save_pretrained(save_path)
enable_full_determinism(seed=42)
model = AutoModelForCausalLM.from_config(
config, torch_dtype=torch.bfloat16, trust_remote_code=True,
)
try:
model.generation_config = GenerationConfig.from_pretrained(
model_id, trust_remote_code=True)
except:
print("No generation config found")
# This fixes the NaN values
model.model.layers[1].mlp.gate.e_score_correction_bias = torch.nn.Parameter(
torch.randn_like(
model.model.layers[1].mlp.gate.e_score_correction_bias) * 1e-2)
num_params = 0
with torch.no_grad():
for name, p in sorted(model.named_parameters()):
if 'experts' in name and 'experts.0.' not in name: # avoid printing too much
pass
else:
print(name, p.shape)
# torch.nn.init.uniform_(p, -0.2, 0.2)
num_params += p.numel()
print(f"Number of parameters: {num_params / 1e6:.2f}M")
model.save_pretrained(save_path)
# patch to use official modeling codes
auto_map = config.auto_map
import json
with open(f"{save_path}/config.json", "r") as f:
config_json = json.load(f)
config_json['auto_map'] = auto_map
with open(f"{save_path}/config.json", "w") as f:
json.dump(config_json, f, indent=2)
! cat {save_path}/config.json
del model
del tokenizer
for p in Path(save_path).glob("*.py"):
os.remove(p)
os.system(f"ls -alh {save_path}")
torch.use_deterministic_algorithms(False)