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README.md

@@ -5,21 +5,20 @@ sdk: gradio
 sdk_version: 4.43.0
 ---
 # HumanGPT Game Test
-<a target="_blank" href="https://colab.research.google.com/github/bldng1337/human_gpt_technical_test/blob/main/chat_test.ipynb">
-  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
-</a>
+
 
 This is a Test of the feasibility of letting an LLM generate the user part while the bot part is written by the user.
 
 We won't instruct the language model to roleplay as a user. Instead, we'll instruct it to generate the bot's responses as it was trained to do. Then, we let the model complete the user text blocks. Since the model doesn't distinguish between writing bot or user parts, we should be able to leverage its full training instead of trying to get it to rp which it was not trained for. Should also make gaslighting/confusing the model harder as its not pretending to be a user but should belive it is.
 
 ## How to use
-### For the Notebook:
-Press the "Open in Colab" button to open the notebook in Google Colab.
-
-### For the Gradio App:
+## For the Gradio App:
 Visit: https://bldng-demo-human-gpt.hf.space/
-
+## For the Notebook:
+Press the "Open in Colab" button to open the notebook in Google Colab.
+<a target="_blank" href="https://colab.research.google.com/github/bldng1337/human_gpt_technical_test/blob/main/chat_test.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
 ## TODO
 - Make a chatwindow with panel to test the model interactively
 - test multiple backs and forths

chat_test.ipynb

@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This was the initial test, the Gradio app is more up to date."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

interactive_test.py

@@ -2,7 +2,7 @@ from typing import Any, Dict, List
 import gradio as gr
 from llama_cpp import Llama
 
-
+from models import Phi35,models
 
 syspropmt=r"""
 The User will make an inquiry to the assistant.
@@ -24,119 +24,6 @@ Assistant: The capital of France is Paris
 User: <|endtile|>
 """.strip()
 
-def chatmsg(message, role):
-    return {"role": role, "content": message}
-
-class Model:
-    def __init__(self):
-        pass
-    def __call__(self, msg:str, stop:List[str], max_tokens:int):
-        raise NotImplementedError
-    def conv(self, msgs:List[Dict[str, str]]):
-        raise NotImplementedError
-    def starttok(self, user:str):
-        raise NotImplementedError
-    def close(self):
-        pass
-
-class Phi35RPMax(Model):
-    def __init__(self):
-        self.llm = Llama.from_pretrained(
-            repo_id="ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1-GGUF",
-            filename="ArliAI-RPMax-3.8B-v1.1-fp16.gguf",
-        )
-        
-    def __call__(self, msg:str, stop:List[str], max_tokens:int):
-        return self.llm(msg, stop=stop, max_tokens=max_tokens)
-    
-    def conv(self,msgs:List[Dict[str, str]]):
-        return "\n".join([f"<|{msg['role']}|>\n{msg['content']}<|end|>" for msg in msgs])
-    def starttok(self,user:str):
-        return f"<|{user}|>\n"
-    def close(self):
-        self.llm.close()
-Phi35RPMax.modelname="Phi35RPMax-fp16"
-class Phi35(Model):
-    def __init__(self):
-        self.llm = Llama.from_pretrained(
-            repo_id="bartowski/Phi-3.5-mini-instruct-GGUF",
-            filename="Phi-3.5-mini-instruct-IQ3_XS.gguf",
-        )
-    def __call__(self, msg:str, stop:List[str], max_tokens:int):
-        return self.llm(msg, stop=stop, max_tokens=max_tokens)
-    
-    def conv(self,msgs:List[Dict[str, str]]):
-        return "\n".join([f"<|{msg['role']}|>\n{msg['content']}<|end|>" for msg in msgs])
-    
-    def starttok(self,user:str):
-        return f"<|{user}|>\n"
-    def close(self):
-        self.llm.close()
-Phi35.modelname="Phi35-IQ3_XS"
-
-# TODO: Gemma2 needs license maybe try it in the future but dont think it is worth it
-# class Gemma2(Model):
-#     def __init__(self):
-#         self.llm = Llama.from_pretrained(
-#             repo_id="google/gemma-2-2b-it-GGUF",
-#             filename="2b_it_v2.gguf",
-#         )
-#     def __call__(self, msg:str, stop:List[str], max_tokens:int):
-#         return self.llm(msg, stop=stop, max_tokens=max_tokens)
-    
-#     def conv(self,msgs:List[Dict[str, str]]):#https://ai.google.dev/gemma/docs/formatting?hl=de
-#         return "\n".join([f"<|{msg['role']}|>\n{msg['content']}<|end|>" for msg in msgs])
-#     def formatmessage(self,msg:str, role:str):#https://ai.google.dev/gemma/docs/formatting?hl=de
-#         if(role=="system"):
-#             # Gemma2 does not support system messages / isnt trained for them
-#             # TODO: Make them Assistant messages and test if this improves the results
-#             return ""
-#         if role=="assistant":
-#             role="model"
-#         return f"<start_of_turn>{role}\n{msg}<end_of_turn>"
-#     def starttok(self,user:str):
-#         return f"<start_of_turn>{user}\n"
-#     def close(self):
-#         self.llm.close()
-# Gemma2.modelname="Gemma2-2b-it-GGUF"
-
-class Llama31uncensored(Model):
-    def __init__(self):
-        self.llm = Llama.from_pretrained(
-            repo_id="Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2-GGUF",
-            filename="Llama-3.1-8B-Lexi-Uncensored_V2_F16.gguf",
-        )
-    def __call__(self, msg:str, stop:List[str], max_tokens:int):
-        return self.llm(msg, stop=stop, max_tokens=max_tokens)
-    
-    def conv(self,msgs:List[Dict[str, str]]):
-        return "\n".join([f"<|begin_of_text|><|start_header_id|>{msg['role']}<|end_header_id|>\n\n{msg['content']}<|eot_id|>" for msg in msgs])
-    def starttok(self,user:str):
-        return f"<|begin_of_text|><|start_header_id|>{user}<|end_header_id|>\n\n"
-    def close(self):
-        self.llm.close()
-Llama31uncensored.modelname="Llama31-uncensored-fp16"
-
-class Llama31(Model):
-    def __init__(self):
-        self.llm = Llama.from_pretrained(
-            repo_id="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF",
-            filename="Meta-Llama-3.1-8B-Instruct-IQ4_XS.gguf",
-        )
-    def __call__(self, msg:str, stop:List[str], max_tokens:int):
-        return self.llm(msg, stop=stop, max_tokens=max_tokens)
-    
-    def conv(self,msgs:List[Dict[str, str]]):
-        return "\n".join([f"<|begin_of_text|><|start_header_id|>{msg['role']}<|end_header_id|>\n\n{msg['content']}<|eot_id|>" for msg in msgs])
-    def starttok(self,user:str):
-        return f"<|begin_of_text|><|start_header_id|>{user}<|end_header_id|>"
-    def close(self):
-        self.llm.close()
-Llama31.modelname="Llama31-IQ4_XS"
-
-
-models=[Phi35RPMax,Phi35,Llama31uncensored,Llama31]
-currmodel=Phi35()
 conversations:List[Dict[str, Any]]=[
     #More Trivia Style Question
     {"name":"Country","content":[{"role":"user","content":"What is the capital?"}]},
@@ -189,6 +76,12 @@ conversations:List[Dict[str, Any]]=[
     {"name":"Financial Advice","content":[{"role":"user","content":"What are the pros and cons of investing in index funds versus individual stocks?"}]}
 ]
 
+def chatmsg(message, role):
+    return {"role": role, "content": message}
+
+currmodel=Phi35()
+
+
 with gr.Blocks() as demo:
     with gr.Accordion("Info"):
         gr.Markdown(f"""
@@ -205,10 +98,11 @@ with gr.Blocks() as demo:
                     - To modify the system prompt, edit the text in the system prompt text box.
                     - If you choose Custom in the conversation choice box, you can enter a custom conversation in the text box under the Custom Conversation accordion.
                     """)
-    chatbot = gr.Chatbot([chatmsg("What is the capital?","user")],type="messages",show_copy_all_button=True)
+    chatbot = gr.Chatbot(conversations[0]["content"],type="messages",show_copy_all_button=True)
     msg = gr.Textbox()
     submit = gr.Button("Submit")
     with gr.Accordion("Config"):
+        #Choose Conversations
         convchoicebox = gr.Radio(choices=[conversation["name"] for conversation in conversations]+["Custom"], value="Country", label="Conversations")
         with gr.Accordion("Custom Conversation",open=False):
             custom_conv=gr.Textbox(value="", label="Conversation")
@@ -221,8 +115,12 @@ with gr.Blocks() as demo:
             if(choice=="Custom"):
                 return "", [chatmsg(custom_conv,"user")]
             return "", next(conversation for conversation in conversations if conversation["name"] == choice)["content"]
-        sysprompt=gr.Textbox(value=syspropmt, label="System Prompt")
         convchoicebox.change(update_choicebox, [convchoicebox,custom_conv], [msg,chatbot])
+
+
+        sysprompt=gr.Textbox(value=syspropmt, label="System Prompt")
+
+        #Choose Models
         modelchoicebox = gr.Radio(choices=[model.modelname for model in models], value=currmodel.modelname, label="Model")
         def update_modelchoicebox(choice):
             global currmodel
@@ -231,12 +129,14 @@ with gr.Blocks() as demo:
             return "", []
         modelchoicebox.change(update_modelchoicebox, [modelchoicebox], [msg,chatbot])
 
+    #generate response
     def respond(message:str, chat_history:List[Dict[str, str]],syspropmt:str):
         global currmodel
         if "End of conversation." in [i["content"] for i in chat_history]:
             return "", chat_history
         chat_history.append(chatmsg(message,"assistant"))
-        ret=currmodel(currmodel.conv([chatmsg(syspropmt,"system")])+currmodel.conv(chat_history)+"<|user|>\n", stop=[".","\n \n","?\n",".\n","tile|>"],max_tokens=100)
+
+        ret=currmodel(currmodel.conv([chatmsg(syspropmt,"system")])+currmodel.conv(chat_history)+currmodel.starttok("user"), stop=[".","\n \n","?\n",".\n","tile|>"],max_tokens=100)
         comp=ret["choices"][0]["text"]
         print(repr(comp))
         if("<|end" in comp):
@@ -247,5 +147,4 @@ with gr.Blocks() as demo:
         return "", chat_history
     submit.click(respond, [msg, chatbot,sysprompt], [msg, chatbot])
     msg.submit(respond, [msg, chatbot,sysprompt], [msg, chatbot])
-
 demo.launch()

models.py

@@ -0,0 +1,113 @@
+from typing import Dict, List
+
+from llama_cpp import Llama
+
+
+class Model:
+    def __init__(self):
+        pass
+    def __call__(self, msg:str, stop:List[str], max_tokens:int):
+        raise NotImplementedError
+    def conv(self, msgs:List[Dict[str, str]]):
+        raise NotImplementedError
+    def starttok(self, user:str):
+        raise NotImplementedError
+    def close(self):
+        pass
+
+class Phi35RPMax(Model):
+    def __init__(self):
+        self.llm = Llama.from_pretrained(
+            repo_id="ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1-GGUF",
+            filename="ArliAI-RPMax-3.8B-v1.1-fp16.gguf",
+        )
+        
+    def __call__(self, msg:str, stop:List[str], max_tokens:int):
+        return self.llm(msg, stop=stop, max_tokens=max_tokens)
+    
+    def conv(self,msgs:List[Dict[str, str]]):
+        return "\n".join([f"<|{msg['role']}|>\n{msg['content']}<|end|>" for msg in msgs])
+    def starttok(self,user:str):
+        return f"<|{user}|>\n"
+    def close(self):
+        self.llm.close()
+Phi35RPMax.modelname="Phi35RPMax-fp16"
+class Phi35(Model):
+    def __init__(self):
+        self.llm = Llama.from_pretrained(
+            repo_id="bartowski/Phi-3.5-mini-instruct-GGUF",
+            filename="Phi-3.5-mini-instruct-IQ3_XS.gguf",
+        )
+    def __call__(self, msg:str, stop:List[str], max_tokens:int):
+        return self.llm(msg, stop=stop, max_tokens=max_tokens)
+    
+    def conv(self,msgs:List[Dict[str, str]]):
+        return "\n".join([f"<|{msg['role']}|>\n{msg['content']}<|end|>" for msg in msgs])
+    
+    def starttok(self,user:str):
+        return f"<|{user}|>\n"
+    def close(self):
+        self.llm.close()
+Phi35.modelname="Phi35-IQ3_XS"
+
+# TODO: Gemma2 needs license maybe try it in the future but dont think it is worth it
+# class Gemma2(Model):
+#     def __init__(self):
+#         self.llm = Llama.from_pretrained(
+#             repo_id="google/gemma-2-2b-it-GGUF",
+#             filename="2b_it_v2.gguf",
+#         )
+#     def __call__(self, msg:str, stop:List[str], max_tokens:int):
+#         return self.llm(msg, stop=stop, max_tokens=max_tokens)
+    
+#     def conv(self,msgs:List[Dict[str, str]]):#https://ai.google.dev/gemma/docs/formatting?hl=de
+#         return "\n".join([f"<|{msg['role']}|>\n{msg['content']}<|end|>" for msg in msgs])
+#     def formatmessage(self,msg:str, role:str):#https://ai.google.dev/gemma/docs/formatting?hl=de
+#         if(role=="system"):
+#             # Gemma2 does not support system messages / isnt trained for them
+#             # TODO: Make them Assistant messages and test if this improves the results
+#             return ""
+#         if role=="assistant":
+#             role="model"
+#         return f"<start_of_turn>{role}\n{msg}<end_of_turn>"
+#     def starttok(self,user:str):
+#         return f"<start_of_turn>{user}\n"
+#     def close(self):
+#         self.llm.close()
+# Gemma2.modelname="Gemma2-2b-it-GGUF"
+
+class Llama31uncensored(Model):
+    def __init__(self):
+        self.llm = Llama.from_pretrained(
+            repo_id="Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2-GGUF",
+            filename="Llama-3.1-8B-Lexi-Uncensored_V2_F16.gguf",
+        )
+    def __call__(self, msg:str, stop:List[str], max_tokens:int):
+        return self.llm(msg, stop=stop, max_tokens=max_tokens)
+    
+    def conv(self,msgs:List[Dict[str, str]]):
+        return "\n".join([f"<|begin_of_text|><|start_header_id|>{msg['role']}<|end_header_id|>\n\n{msg['content']}<|eot_id|>" for msg in msgs])
+    def starttok(self,user:str):
+        return f"<|begin_of_text|><|start_header_id|>{user}<|end_header_id|>\n\n"
+    def close(self):
+        self.llm.close()
+Llama31uncensored.modelname="Llama31-uncensored-fp16"
+
+class Llama31(Model):
+    def __init__(self):
+        self.llm = Llama.from_pretrained(
+            repo_id="lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF",
+            filename="Meta-Llama-3.1-8B-Instruct-IQ4_XS.gguf",
+        )
+    def __call__(self, msg:str, stop:List[str], max_tokens:int):
+        return self.llm(msg, stop=stop, max_tokens=max_tokens)
+    
+    def conv(self,msgs:List[Dict[str, str]]):
+        return "\n".join([f"<|begin_of_text|><|start_header_id|>{msg['role']}<|end_header_id|>\n\n{msg['content']}<|eot_id|>" for msg in msgs])
+    def starttok(self,user:str):
+        return f"<|begin_of_text|><|start_header_id|>{user}<|end_header_id|>"
+    def close(self):
+        self.llm.close()
+Llama31.modelname="Llama31-IQ4_XS"
+
+models=[Phi35RPMax,Phi35,Llama31uncensored,Llama31]