This model is for debugging. It is randomly initialized using the config from meta-llama/Llama-3.2-3B-Instruct but with smaller size.
Codes:
import os
import torch
import transformers
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline, set_seed
model_id = "meta-llama/Llama-3.2-3B-Instruct"
repo_id = "yujiepan/llama-3.2-tiny-random"
save_path = f"/tmp/{repo_id}"
config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
config._name_or_path = model_id
config.hidden_size = 8
config.intermediate_size = 16
config.num_attention_heads = 2
config.num_key_value_heads = 1
config.head_dim = 4
config.num_hidden_layers = 2
config.torch_dtype = "bfloat16"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
tokenizer.save_pretrained(save_path)
model = AutoModelForCausalLM.from_config(
config, torch_dtype=torch.bfloat16, attn_implementation="sdpa", trust_remote_code=True
)
model.generation_config = GenerationConfig.from_pretrained(
model_id, trust_remote_code=True)
set_seed(42)
with torch.no_grad():
for _, p in sorted(model.named_parameters()):
torch.nn.init.uniform_(p, -0.2, 0.2)
model.save_pretrained(save_path)
pipe = pipeline("text-generation", model=save_path, device="cpu",
trust_remote_code=True, max_new_tokens=20)
print(pipe("Hello World!"))