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import torch
import torch.nn as nn
from model import TransformerModel # or however you define your model classes
from transformers import AutoTokenizer
import gradio as gr
# Load half-precision state_dict
checkpoint = torch.load("model_weights_fp16.pt", map_location="cpu")
state_dict_fp16 = checkpoint["model_state_dict"]
# Create model in FP16
model = TransformerModel(
vocab_size=49152,
hidden_size=576,
num_hidden_layers=30,
num_attention_heads=9,
intermediate_size=1536,
num_key_value_heads=3,
max_position_embeddings=2048,
rms_norm_eps=1e-5,
hidden_act="silu",
tie_word_embeddings=True,
)
# Convert model to half precision
model.half()
# Load the half-precision weights
model.load_state_dict(state_dict_fp16, strict=False)
model.eval()
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/cosmo2-tokenizer")
def generate_text(prompt, max_length=50):
input_ids = tokenizer.encode(prompt, return_tensors="pt")
with torch.no_grad():
output_ids = model.generate(input_ids, max_length=max_length, do_sample=True)
return tokenizer.decode(output_ids[0], skip_special_tokens=True)
gr.Interface(fn=generate_text, inputs="text", outputs="text").launch()
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