Delete app.py

app.py

@@ -1,163 +0,0 @@
-import torch
-from peft import PeftModel, PeftConfig
-import transformers
-import gradio as gr
-
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, BloomForCausalLM, GenerationConfig
-from transformers.models.opt.modeling_opt import OPTDecoderLayer
-
-tokenizer = AutoTokenizer.from_pretrained('bigscience/bloom')
-
-BASE_MODEL = "bigscience/bloom-3b"
-
-#LORA_WEIGHTS = f"/content/drive/MyDrive/Colab Notebooks/LegalChatbot-{model_name}"
-LORA_WEIGHTS = f"jslin09/LegalChatbot-bloom-3b"
-
-config = PeftConfig.from_pretrained(LORA_WEIGHTS)
-
-if torch.cuda.is_available():
-    device = "cuda"
-else:
-    device = "cpu"
-
-try:
-    if torch.backends.mps.is_available():
-        device = "mps"
-except:
-    pass
-
-if device == "cuda":
-    model = BloomForCausalLM.from_pretrained(
-        BASE_MODEL,
-        load_in_8bit=True,
-        torch_dtype=torch.float16,
-        device_map="auto",
-    )
-    model = PeftModel.from_pretrained(model, LORA_WEIGHTS, torch_dtype=torch.float16)
-elif device == "mps":
-    model = BloomForCausalLM.from_pretrained(
-        BASE_MODEL,
-        device_map={"": device},
-        torch_dtype=torch.float16,
-    )
-    model = PeftModel.from_pretrained(
-        model,
-        LORA_WEIGHTS,
-        device_map={"": device},
-        torch_dtype=torch.float16,
-    )
-else:
-    model = BloomForCausalLM.from_pretrained(
-        BASE_MODEL, device_map={"": device},
-        low_cpu_mem_usage=True
-    )
-    model = PeftModel.from_pretrained(
-        model,
-        LORA_WEIGHTS,
-        device_map={"": device},
-    )
-
-
-def generate_prompt(instruction, input=None):
-    if input:
-        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
-
-### Instruction:
-{instruction}
-
-### Input:
-{input}
-
-### Response:"""
-    else:
-        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
-
-### Instruction:
-{instruction}
-
-### Response:"""
-
-def generate_prompt_tw(instruction, input=None):
-    if input:
-        return f"""以下是描述任務的指令，並與提供進一步上下文的輸入配對。編寫適當完成請求的回應。
-
-### 指令:
-{instruction}
-
-### 輸入:
-{input}
-
-### 回應:"""
-    else:
-        return f"""以下是描述任務的指令。編寫適當完成請求的回應。
-
-### 指令:
-{instruction}
-
-### 回應:"""
-
-
-model.eval()
-if torch.__version__ >= "2":
-    model = torch.compile(model)
-
-
-def evaluate(
-    instruction,
-    input=None,
-    temperature=0.1,
-    top_p=0.75,
-    top_k=40,
-    num_beams=4,
-    max_new_tokens=128,
-    **kwargs,
-):
-    prompt = generate_prompt_tw(instruction, input) # 中文版的話，函數名稱要改用 generate_prompt_tw
-    inputs = tokenizer(prompt, return_tensors="pt")
-    input_ids = inputs["input_ids"].to(device)
-    generation_config = GenerationConfig(
-        temperature=temperature,
-        top_p=top_p,
-        top_k=top_k,
-        num_beams=num_beams,
-        **kwargs,
-    )
-    with torch.no_grad():
-        generation_output = model.generate(
-            input_ids=input_ids,
-            generation_config=generation_config,
-            return_dict_in_generate=True,
-            output_scores=True,
-            max_new_tokens=max_new_tokens,
-        )
-    s = generation_output.sequences[0]
-    output = tokenizer.decode(s)
-#    return output.split("### Response:")[1].strip() # 中文版的話，要改為 return output.split("### 回應:")[1].strip()
-    return output.split("### 回應:")[1].strip()
-
-
-gr.Interface(
-    fn=evaluate,
-    inputs=[
-        gr.components.Textbox(
-            lines=2, label="Instruction", placeholder="Tell me about alpacas."
-        ),
-        gr.components.Textbox(lines=2, label="Input", placeholder="none"),
-        gr.components.Slider(minimum=0, maximum=1, value=0.1, label="Temperature"),
-        gr.components.Slider(minimum=0, maximum=1, value=0.75, label="Top p"),
-        gr.components.Slider(minimum=0, maximum=100, step=1, value=40, label="Top k"),
-        gr.components.Slider(minimum=1, maximum=4, step=1, value=4, label="Beams"),
-        gr.components.Slider(
-            minimum=1, maximum=2000, step=1, value=128, label="Max tokens"
-        ),
-    ],
-    outputs=[
-        gr.components.Textbox(
-            lines=5,
-            label="Output",
-        )
-    ],
-    title="🌲 🌲 🌲 BLOOM-LoRA-LegalChatbot",
-    description="BLOOM-LoRA-LegalChatbot is a 3B-parameter BLOOM model finetuned to follow instructions. It is trained on the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) dataset and my Legal QA dataset, and makes use of the Huggingface BLOOM implementation. For more information, please visit [the project's website](https://github.com/tloen/alpaca-lora).",
-).launch()
-