Create app.py

app.py

@@ -0,0 +1,162 @@
+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()