add more models
Browse files- .gitignore +1 -0
- app.py +18 -26
- context_window.json +31 -21
.gitignore
CHANGED
@@ -1,3 +1,4 @@
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*.env
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*.venv
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*.pem
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*.env
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*.venv
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*.pem
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*.ipynb
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app.py
CHANGED
@@ -6,36 +6,25 @@ import os
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import random
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import threading
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import aisuite as ai
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import gradio as gr
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import pandas as pd
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from huggingface_hub import upload_file, hf_hub_download, HfFolder, HfApi
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from datetime import datetime
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from gradio_leaderboard import Leaderboard
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# Load environment variables
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dotenv.load_dotenv()
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#
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#
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credentials_path = (
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"/tmp/gcp_credentials.json" # Ensure this path is secure and temporary
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)
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with open(credentials_path, "w") as f:
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f.write(gcp_credentials)
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# Set the environment variable for GCP SDKs
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os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = credentials_path
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# Timeout in seconds for model response
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TIMEOUT = 60
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# Initialize AISuite Client
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client = ai.Client()
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# Hint string constant
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SHOW_HINT_STRING = True # Set to False to hide the hint string altogether
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HINT_STRING = "Once signed in, your votes will be recorded securely."
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@@ -75,7 +64,9 @@ def truncate_prompt(user_input, model_alias, models, conversation_state):
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# Get the full conversation history for the model
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history = conversation_state.get(model_name, [])
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full_conversation = [
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full_conversation.append({"role": "user", "content": user_input})
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# Convert to JSON string for accurate length measurement
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@@ -100,7 +91,9 @@ def chat_with_models(
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user_input, model_alias, models, conversation_state, timeout=TIMEOUT
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):
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model_name = models[model_alias]
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truncated_input = truncate_prompt(
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conversation_state.setdefault(model_name, []).append(
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{"role": "user", "content": user_input}
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)
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@@ -110,10 +103,12 @@ def chat_with_models(
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def request_model_response():
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try:
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model
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messages
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model_response["content"] = response.choices[0].message.content
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except Exception as e:
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model_response["error"] = f"{model_name} model is not available. Error: {e}"
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@@ -128,15 +123,12 @@ def chat_with_models(
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response_event_occurred = response_event.wait(timeout)
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if not response_event_occurred:
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# Timeout occurred, raise a TimeoutError to be handled in the Gradio interface
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raise TimeoutError(
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f"The {model_alias} model did not respond within {timeout} seconds."
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)
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elif model_response["error"]:
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# An error occurred during model response
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raise Exception(model_response["error"])
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else:
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# Successful response
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formatted_response = f"```\n{model_response['content']}\n```"
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conversation_state[model_name].append(
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{"role": "assistant", "content": model_response["content"]}
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import random
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import threading
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import gradio as gr
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import pandas as pd
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from huggingface_hub import upload_file, hf_hub_download, HfFolder, HfApi
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from datetime import datetime
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from gradio_leaderboard import Leaderboard
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from openai import OpenAI
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# Load environment variables
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dotenv.load_dotenv()
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# Initialize OpenAI Client
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api_key = os.getenv("API_KEY")
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base_url = "https://api.pandalla.ai/v1"
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openai_client = OpenAI(api_key=api_key, base_url=base_url)
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# Timeout in seconds for model responses
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TIMEOUT = 60
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# Hint string constant
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SHOW_HINT_STRING = True # Set to False to hide the hint string altogether
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HINT_STRING = "Once signed in, your votes will be recorded securely."
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# Get the full conversation history for the model
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history = conversation_state.get(model_name, [])
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full_conversation = [
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{"role": msg["role"], "content": msg["content"]} for msg in history
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]
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full_conversation.append({"role": "user", "content": user_input})
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# Convert to JSON string for accurate length measurement
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user_input, model_alias, models, conversation_state, timeout=TIMEOUT
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):
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model_name = models[model_alias]
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truncated_input = truncate_prompt(
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user_input, model_alias, models, conversation_state
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)
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conversation_state.setdefault(model_name, []).append(
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{"role": "user", "content": user_input}
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)
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def request_model_response():
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try:
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request_params = {
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"model": model_name,
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"messages": truncated_input,
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"temperature": 0,
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}
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response = openai_client.chat.completions.create(**request_params)
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model_response["content"] = response.choices[0].message.content
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except Exception as e:
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model_response["error"] = f"{model_name} model is not available. Error: {e}"
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response_event_occurred = response_event.wait(timeout)
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if not response_event_occurred:
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raise TimeoutError(
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f"The {model_alias} model did not respond within {timeout} seconds."
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)
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elif model_response["error"]:
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raise Exception(model_response["error"])
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else:
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formatted_response = f"```\n{model_response['content']}\n```"
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conversation_state[model_name].append(
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{"role": "assistant", "content": model_response["content"]}
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context_window.json
CHANGED
@@ -1,23 +1,33 @@
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{
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}
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{
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+
"gpt-3.5-turbo": 16000,
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+
"gpt-3.5-turbo-16k": 16000,
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"gpt-3.5-turbo-instruct" : 16000,
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"gpt-4": 8192,
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"gpt-4-32k": 32000,
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+
"gpt-4-turbo": 128000,
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"gpt-4o": 128000,
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"gpt-4o-mini": 128000,
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"chatgpt-4o-latest": 128000,
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"claude-3-5-sonnet-latest" : 200000,
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"deepseek-chat": 64000,
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"gemini-1.5-flash-latest": 1048576,
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"gemini-1.5-pro-latest": 2097152,
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"Hunyuan-A52B-Instruct": 128000,
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"llama-3-70b": 128000,
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"llama-3.1-405b": 128000,
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"llama-3.1-70b": 128000,
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"llama-3.1-8b": 128000,
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"llama-3.3-70b": 128000,
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"llama-v3.2-3b": 128000,
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"o1-all": 128000,
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"o1-mini-all": 128000,
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"o1-preview-all": 128000,
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"Qwen2-72B-Instruct": 131072,
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"Qwen2.5-32B-Instruct": 131072,
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+
"qwen2.5-72b": 32768,
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+
"Qwen2.5-72B-Instruct": 131072,
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"Qwen2.5-72B-Instruct-128k": 131072,
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"Qwen2.5-Coder-32B-Instruct": 131072,
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"QwQ-32B-Preview": 32768,
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"yi-large": 32000
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}
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