Update app.py

app.py

@@ -5,26 +5,16 @@ import transformers
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import os
 
-title = """# Welcome to 🌟Tonic's🐇🥷🏻Trinity
+title = """# Welcome to 🌟Tonic's🐇🥷🏻Neo
 You can build with this endpoint using🐇🥷🏻Trinity available here : [WhiteRabbitNeo/Trinity-13B](https://huggingface.co//WhiteRabbitNeo/Trinity-13B). You can also use 🐇🥷🏻Trinity by cloning this space. Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/trinity?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> 
 Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's 🛠️community 👻 [![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/GWpVpekp) On 🤗Huggingface:[MultiTransformer](https://huggingface.co/MultiTransformer) Math 🔍 [introspector](https://huggingface.co/introspector) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to🌟 [SciTonic](https://github.com/Tonic-AI/scitonic)🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
 """
 
 
-default_system_prompt = """
-Answer the Question by exploring multiple reasoning paths as follows:
-- First, carefully analyze the question to extract the key information components and break it down into logical sub-questions. This helps set up the framework for reasoning. The goal is to construct an internal search tree.
-- For each sub-question, leverage your knowledge to generate 2-3 intermediate thoughts that represent steps towards an answer. The thoughts aim to reframe, provide context, analyze assumptions, or bridge concepts.
-- Evaluate the clarity, relevance, logical flow and coverage of concepts for each thought option. Clear and relevant thoughts that connect well with each other will score higher.
-- Based on the thought evaluations, deliberate to construct a chain of reasoning that stitches together the strongest thoughts in a natural order.
-- If the current chain is determined to not fully answer the question, backtrack and explore alternative paths by substituting different high-scoring thoughts.
-- Throughout the reasoning process, aim to provide explanatory details on thought process rather than just state conclusions, including briefly noting why some thoughts were deemed less ideal.
-- Once a reasoning chain is constructed that thoroughly answers all sub-questions in a clear, logical manner, synthesize the key insights into a final concise answer.
-- Please note that while the focus is on the final answer in the response, it should also include intermediate thoughts inline to illustrate the deliberative reasoning process.
-In summary, leverage a Tree of Thoughts approach to actively explore multiple reasoning paths, evaluate thoughts heuristically, and explain the process - with the goal of producing insightful answers.
-"""
+default_system_prompt = """SYSTEM: You are an AI that code. Answer with code."""
+
+model_path = "whiterabbitneo/WhiteRabbitNeo-33B-v-1"
 
-model_path = "WhiteRabbitNeo/Trinity-13B"
 
 hf_token = os.getenv("HF_TOKEN")
 if not hf_token:
@@ -34,7 +24,8 @@ model = AutoModelForCausalLM.from_pretrained(
     model_path,
     torch_dtype=torch.float16,
     device_map="auto",
-    load_in_8bit=True,
+    load_in_4bit=True,
+    load_in_8bit=False,
     trust_remote_code=True,
 )
 
@@ -67,7 +58,7 @@ def gradio_app():
     with gr.Blocks() as demo:
         gr.Markdown(title)
         with gr.Row():
-            custom_prompt = gr.Textbox(label="Custom System Prompt (optional)", placeholder="Leave blank to use the default prompt...")
+            custom_prompt = gr.Textbox(label="🐇🥷🏻NeoCustom System Prompt (optional)", placeholder="Leave blank to use the default prompt...")
             instruction = gr.Textbox(label="Your Instruction", placeholder="Type your question here...")
         with gr.Row():
             temperature = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, value=0.5, label="Temperature")
@@ -76,7 +67,7 @@ def gradio_app():
             top_k = gr.Slider(minimum=0, maximum=100, step=1, value=50, label="Top K")
         with gr.Row():
             generate_btn = gr.Button("Generate")
-        output = gr.Textbox(label="Generated Text", lines=10, placeholder="Generated answer will appear here...")
+        output = gr.Code(label="Generated Text", lines=10, placeholder="🐇🥷🏻Neo:")
 
         generate_btn.click(
             fn=generate_text,