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Browse files- app.py +40 -0
- requirements.txt +1 -0
app.py
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import gradio as gr
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from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
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model_path = "vinai/PhoGPT-7B5-Instruct"
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config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
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config.init_device = "cuda"
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# config.attn_config['attn_impl'] = 'triton' # Enable if "triton" installed!
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model = AutoModelForCausalLM.from_pretrained(
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model_path, config=config, torch_dtype=torch.bfloat16, trust_remote_code=True
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)
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# If your GPU does not support bfloat16:
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# model = AutoModelForCausalLM.from_pretrained(model_path, config=config, torch_dtype=torch.float16, trust_remote_code=True)
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model.eval()
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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def answer(input_prompt):
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input_ids = tokenizer(input_prompt, return_tensors="pt")
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outputs = model.generate(
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inputs=input_ids["input_ids"].to("cuda"),
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attention_mask=input_ids["attention_mask"].to("cuda"),
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do_sample=True,
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temperature=1.0,
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top_k=50,
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top_p=0.9,
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max_new_tokens=1024,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.pad_token_id
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)
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response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
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response = response.split("### Trả lời:")[1]
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return response
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iface = gr.Interface(fn=greet, inputs="text", outputs="text")
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iface.launch()
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requirements.txt
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gradio
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