add app

app.py

@@ -0,0 +1,116 @@
+# %%
+import gradio as gr
+from transformers import LlamaTokenizer
+from transformers import LlamaForCausalLM, GenerationConfig
+from peft import PeftModel
+import torch
+
+
+def generate_instruction_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 evaluate(
+    model,
+    tokenizer,
+    instruction,
+    input=None,
+    temperature=0.1,
+    top_p=0.75,
+    num_beams=4,
+    max_token=256,
+):
+    generation_config = GenerationConfig(
+        temperature=temperature,
+        top_p=top_p,
+        num_beams=num_beams,
+        top_k=40,
+        no_repeat_ngram_size=3,
+    )
+    prompt = generate_instruction_prompt(instruction, input)
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].cuda()
+    generation_output = model.generate(
+        input_ids=input_ids,
+        generation_config=generation_config,
+        return_dict_in_generate=True,
+        output_scores=True,
+        max_new_tokens=max_token,
+    )
+    s = generation_output.sequences[0]
+    output = tokenizer.decode(s)
+    res = output.split("### Response:")[1].strip()
+    print("Response:", res)
+    return res
+
+
+def load_lora(lora_path, base_model="decapoda-research/llama-7b-hf"):
+    model = LlamaForCausalLM.from_pretrained(
+        base_model,
+        # load_in_8bit=True,
+        device_map="auto",
+        low_cpu_mem_usage=True,
+    )
+    lora = PeftModel.from_pretrained(
+        model,
+        lora_path,
+        device_map="auto",
+    )
+    return lora
+
+
+base_model = "decapoda-research/llama-13b-hf"
+tokenizer = LlamaTokenizer.from_pretrained(base_model, cache_dir="data/hf")
+# question = "如果今天是星期五, 那么后天是星期几?"
+model = load_lora(lora_path="facat/alpaca-lora-cn-13b", base_model=base_model)
+
+eval = lambda question, input, temperature, beams, max_token: evaluate(
+    model,
+    tokenizer,
+    question,
+    input=input,
+    temperature=temperature,
+    num_beams=beams,
+    max_token=max_token,
+)
+
+gr.Interface(
+    fn=eval,
+    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=512, step=1, value=256, label="Max tokens"
+        ),
+    ],
+    outputs=[
+        gr.inputs.Textbox(
+            lines=8,
+            label="Output",
+        )
+    ],
+    title=f"Alpaca-LoRA",
+    description=f"Alpaca-LoRA",
+).launch()