Create app.py

app.py

@@ -0,0 +1,134 @@
+from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM
+from peft import PeftModel, PeftConfig
+import torch
+import gradio as gr
+import random
+from textwrap import wrap
+
+EXAMPLES = [
+    ["Hey Falcon! Any recommendations for my holidays in Abu Dhabi?"],
+    ["What's the Everett interpretation of quantum mechanics?"],
+    ["Give me a list of the top 10 dive sites you would recommend around the world."],
+    ["Can you tell me more about deep-water soloing?"],
+    ["Can you write a short tweet about the release of our latest AI model, Falcon LLM?"]
+    ]
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+base_model_id = "tiiuae/falcon-7b-instruct"
+model_directory = "Tonic/GaiaMiniMed"
+
+tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True, padding_side="left")
+model_config = AutoConfig.from_pretrained(base_model_id)
+peft_model = AutoModelForCausalLM.from_pretrained(model_directory, config=model_config)
+peft_model = PeftModel.from_pretrained(peft_model, model_directory)
+
+def format_prompt(message, history, system_prompt):
+  prompt = ""
+  if system_prompt:
+    prompt += f"System: {system_prompt}\n"
+  for user_prompt, bot_response in history:
+    prompt += f"User: {user_prompt}\n"
+    prompt += f"Falcon: {bot_response}\n" # Response already contains "Falcon: "
+  prompt += f"""User: {message}
+Falcon:"""
+  return prompt
+
+seed = 42
+
+def peft_model.generate(
+    prompt, history, system_prompt="", temperature=0.9, max_new_tokens=500, top_p=0.95, repetition_penalty=1.0,
+):
+    temperature = float(temperature)
+    if temperature < 1e-2:
+        temperature = 1e-2
+    top_p = float(top_p)
+    global seed
+    generate_kwargs = dict(
+        temperature=temperature,
+        max_new_tokens=max_new_tokens,
+        top_p=top_p,
+        repetition_penalty=1.0,
+        stop_sequences="[END]",
+        do_sample=True,
+        seed=seed,
+    )
+    seed = seed + 1
+    formatted_prompt = format_prompt(prompt, history, system_prompt)
+
+    try:
+        stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+        output = ""
+
+        for response in stream:
+            output += response.token.text
+    
+            for stop_str in STOP_SEQUENCES:
+                if output.endswith(stop_str):
+                    output = output[:-len(stop_str)]
+                    output = output.rstrip()
+                    yield output
+            yield output
+    except Exception as e:
+        raise gr.Error(f"Error while generating: {e}")
+    return output
+
+
+additional_inputs=[
+    gr.Textbox("", label="Optional system prompt"),
+    gr.Slider(
+        label="Temperature",
+        value=0.9,
+        minimum=0.0,
+        maximum=1.0,
+        step=0.05,
+        interactive=True,
+        info="Higher values produce more diverse outputs",
+    ),
+    gr.Slider(
+        label="Max new tokens",
+        value=256,
+        minimum=0,
+        maximum=3000,
+        step=64,
+        interactive=True,
+        info="The maximum numbers of new tokens",
+    ),
+    gr.Slider(
+        label="Top-p (nucleus sampling)",
+        value=0.90,
+        minimum=0.01,
+        maximum=0.99,
+        step=0.05,
+        interactive=True,
+        info="Higher values sample more low-probability tokens",
+    ),
+    gr.Slider(
+        label="Repetition penalty",
+        value=1.2,
+        minimum=1.0,
+        maximum=2.0,
+        step=0.05,
+        interactive=True,
+        info="Penalize repeated tokens",
+    )
+]
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column(scale=0.4):
+            gr.Image("better_banner.jpeg", elem_id="banner-image", show_label=False)
+        with gr.Column():
+            gr.Markdown(
+            # 👋🏻Welcome to Tonic's GaiaMiniMed Chat🚀"
+            "You can use this Space to test out the current model [(Tonic/GaiaMiniMed)](https://huggingface.co/Tonic/GaiaMiniMed) or duplicate this Space and use it locally or on 🤗HuggingFace. [Join me on Discord to build together](https://discord.gg/VqTxc76K3u)."
+            )
+
+    gr.ChatInterface(
+        generate, 
+        examples=EXAMPLES,
+        additional_inputs=additional_inputs,
+    ) 
+
+demo.queue(concurrency_count=100, api_open=False).launch(show_api=False)