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from transformers import AutoModelForCausalLM, AutoTokenizer
import os
hf_token = os.environ.get("HF_TOKEN")
model = AutoModelForCausalLM.from_pretrained(
"Qwen/CodeQwen1.5-7B-Chat",
torch_dtype="auto",
device_map="auto",
token=hf_token
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/CodeQwen1.5-7B-Chat", token=hf_token)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
]
import gradio as gr
def greet(prompt):
messages.append({"role": "user", "content": prompt})
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt")
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0].text
messages.append({"role": "bot", "content": response})
return response
demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch()
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