This tiny model is for debugging. It is randomly initialized with the config adapted from HuggingFaceTB/SmolLM3-3B.
Example usage:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "tiny-random/smollm3"
device = "cuda" # for GPU usage or "cpu" for CPU usage
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True
).to(device)
# prepare the model input
prompt = "Give me a brief explanation of gravity in simple terms."
messages_think = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages_think,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate the output
generated_ids = model.generate(**model_inputs, max_new_tokens=200)
# Get and decode the output
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):]
print(tokenizer.decode(output_ids, skip_special_tokens=True))
Codes to create this repo:
import json
from pathlib import Path
import accelerate
import torch
from huggingface_hub import file_exists, hf_hub_download
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoProcessor,
GenerationConfig,
set_seed,
)
source_model_id = "HuggingFaceTB/SmolLM3-3B"
save_folder = "/tmp/tiny-random/smollm3"
processor = AutoProcessor.from_pretrained(source_model_id, trust_remote_code=True)
processor.save_pretrained(save_folder)
with open(hf_hub_download(source_model_id, filename='config.json', repo_type='model'), 'r', encoding='utf-8') as f:
config_json = json.load(f)
config_json['hidden_size'] = 64
config_json['intermediate_size'] = 128
config_json['num_attention_heads'] = 2
config_json['num_hidden_layers'] = 2
config_json['num_key_value_heads'] = 1
config_json['tie_word_embeddings'] = True
config_json['layer_types'] = None
config_json['no_rope_layer_interval'] = 2
config_json['use_sliding_window'] = True
config_json['sliding_window'] = 128
config_json['use_cache'] = True
config_json['layer_types'] = None
config_json['no_rope_layers'] = None
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)
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() # cpu is more stable for random initialization across machines
with torch.no_grad():
for name, p in sorted(model.named_parameters()):
torch.nn.init.normal_(p, 0, 0.2)
print(name, p.shape)
model.save_pretrained(save_folder)
print(model)
Printing the model:
SmolLM3ForCausalLM(
(model): SmolLM3Model(
(embed_tokens): Embedding(128256, 64, padding_idx=128004)
(layers): ModuleList(
(0-1): 2 x SmolLM3DecoderLayer(
(self_attn): SmolLM3Attention(
(q_proj): Linear(in_features=64, out_features=64, bias=False)
(k_proj): Linear(in_features=64, out_features=32, bias=False)
(v_proj): Linear(in_features=64, out_features=32, bias=False)
(o_proj): Linear(in_features=64, out_features=64, bias=False)
)
(mlp): SmolLM3MLP(
(gate_proj): Linear(in_features=64, out_features=128, bias=False)
(up_proj): Linear(in_features=64, out_features=128, bias=False)
(down_proj): Linear(in_features=128, out_features=64, bias=False)
(act_fn): SiLU()
)
(input_layernorm): SmolLM3RMSNorm((64,), eps=1e-06)
(post_attention_layernorm): SmolLM3RMSNorm((64,), eps=1e-06)
)
)
(norm): SmolLM3RMSNorm((64,), eps=1e-06)
(rotary_emb): SmolLM3RotaryEmbedding()
)
(lm_head): Linear(in_features=64, out_features=128256, bias=False)
)