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import torch
from transformers import (
AutoTokenizer,
AutoModelForCausalLM
)
MODEL_ID = "jerinaj/lfm-tool-merged"
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(
MODEL_ID
)
# Load model
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
dtype=torch.bfloat16,
device_map="auto"
)
model.eval()
def predict(messages):
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = tokenizer(
prompt,
return_tensors="pt"
)
inputs = {
k: v.to(model.device)
for k, v in inputs.items()
}
with torch.inference_mode():
output = model.generate(
**inputs,
max_new_tokens=256,
temperature=0.1,
do_sample=True
)
response = tokenizer.decode(
output[0][inputs["input_ids"].shape[1]:],
skip_special_tokens=True
)
return response
demo = gr.Interface(
fn=predict,
inputs=gr.JSON(),
outputs=gr.Textbox()
)
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=True
) |