This tiny model is intended for debugging. It is randomly initialized using the configuration adapted from PleIAs/Baguettotron.
Example usage:
from transformers import pipeline
model_id = "yujiepan/baguettotron-tiny-random"
pipe = pipeline(
"text-generation", model=model_id, device="cuda",
trust_remote_code=True, max_new_tokens=3,
)
print(pipe("Hello World!"))
Codes to create this repo:
import torch
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
GenerationConfig,
pipeline,
set_seed,
)
source_model_id = "PleIAs/Baguettotron"
save_folder = "/tmp/yujiepan/baguettotron-tiny-random"
tokenizer = AutoTokenizer.from_pretrained(
source_model_id, trust_remote_code=True,
)
tokenizer.chat_template = "{% for m in messages %}<|im_start|>{{ m['role'] }}\n{{ m['content'] }}<|im_end|>\n{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n<think>\n{% endif %}"
tokenizer.eos_token = "<|im_end|>"
tokenizer.bos_token = "<|im_start|>"
tokenizer.stop_token = "<|im_end|>"
tokenizer.save_pretrained(save_folder)
config = AutoConfig.from_pretrained(
source_model_id, trust_remote_code=True,
)
config.hidden_size = 8
config.intermediate_size = 64
config.num_attention_heads = 16
config.num_key_value_heads = 8
config.head_dim = 32
config.num_hidden_layers = 2
model = AutoModelForCausalLM.from_config(
config,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
)
model.generation_config = GenerationConfig.from_pretrained(
source_model_id, trust_remote_code=True,
)
set_seed(42)
model = model.cpu()
with torch.no_grad():
for name, p in sorted(model.named_parameters()):
torch.nn.init.normal_(p, 0, 0.1)
print(name, p.shape)
model.save_pretrained(save_folder)
Printing the model:
LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(65536, 8)
(layers): ModuleList(
(0-1): 2 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear(in_features=8, out_features=512, bias=False)
(k_proj): Linear(in_features=8, out_features=256, bias=False)
(v_proj): Linear(in_features=8, out_features=256, bias=False)
(o_proj): Linear(in_features=512, out_features=8, bias=False)
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=8, out_features=64, bias=False)
(up_proj): Linear(in_features=8, out_features=64, bias=False)
(down_proj): Linear(in_features=64, out_features=8, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): LlamaRMSNorm((8,), eps=1e-05)
(post_attention_layernorm): LlamaRMSNorm((8,), eps=1e-05)
)
)
(norm): LlamaRMSNorm((8,), eps=1e-05)
(rotary_emb): LlamaRotaryEmbedding()
)
(lm_head): Linear(in_features=8, out_features=65536, bias=False)
)