This model is for debugging. It is randomly initialized with the config from deepseek-ai/DeepSeek-V3 but is of smaller size.
⚠️Note: At this moment, this repo does not contain the Multi-Token Prediction (MTP) module as explained here.
Usage:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "yujiepan/deepseek-v3-tiny-random"
device = torch.device("cuda")
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id, trust_remote_code=True,
).eval().to(device)
prompt = 'Hello!'
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
inputs = tokenizer.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(device)
with torch.inference_mode():
outputs = model.generate(
inputs,
max_new_tokens=16,
do_sample=False,
use_cache=True,
)
string = tokenizer.decode(outputs[0])
print(string)
Codes:
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 = "yujiepan/deepseek-v3-tiny-random"
save_path = f"/tmp/{repo_id}"
os.system(f"rm -rf {save_path}")
config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
config.num_hidden_layers = 2
config.first_k_dense_replace = 1
config.hidden_size = 16
config.intermediate_size = 32
config.moe_intermediate_size = 16
config.q_lora_rank = 16
config.kv_lora_rank = 16
config.qk_rope_head_dim = 16
config.qk_nope_head_dim = 16
config.v_head_dim = 16
config.num_attention_heads = 2
config.num_key_value_heads = 2
# 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,
).eval()
try:
model.generation_config = GenerationConfig.from_pretrained(
model_id, trust_remote_code=True)
except:
print("No generation config found")
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.load(f)
config['auto_map'] = auto_map
with open(f"{save_path}/config.json", "w") as f:
json.dump(config, 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)
tokenizer = AutoTokenizer.from_pretrained(save_path)
model = AutoModelForCausalLM.from_pretrained(
save_path, trust_remote_code=True).eval()
prompt = 'Hello!'
messages = [
{"role": "system", "content": "You are a helpful assistant."}
]
messages.append({"role": "user", "content": prompt})
tokenized_chat = tokenizer.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
device = torch.device("cuda")
outputs = model.to(device).generate(
tokenized_chat.to(device),
max_new_tokens=16,
do_sample=False,
use_cache=True,
)
tokens = tokenizer.convert_ids_to_tokens(outputs[0])
string = tokenizer.decode(outputs[0])
print(tokens)
# create_repo(repo_id, exist_ok=True)
# upload_folder(repo_id=repo_id, folder_path=save_path)