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from pydantic import BaseModel |
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from llama_cpp import Llama |
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from concurrent.futures import ThreadPoolExecutor, as_completed |
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import re |
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import httpx |
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import asyncio |
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import gradio as gr |
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import os |
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from spaces import GPU |
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from dotenv import load_dotenv |
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load_dotenv() |
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HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN") |
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global_data = { |
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'models': {}, |
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'tokens': { |
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'eos': 'eos_token', |
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'pad': 'pad_token', |
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'padding': 'padding_token', |
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'unk': 'unk_token', |
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'bos': 'bos_token', |
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'sep': 'sep_token', |
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'cls': 'cls_token', |
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'mask': 'mask_token' |
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} |
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} |
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model_configs = [ |
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{"repo_id": "Ffftdtd5dtft/gpt2-xl-Q2_K-GGUF", "filename": "gpt2-xl-q2_k.gguf", "name": "GPT-2 XL"}, |
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{"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-instruct-q2_k.gguf", "name": "Meta Llama 3.1-8B Instruct"}, |
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{"repo_id": "Ffftdtd5dtft/gemma-2-9b-it-Q2_K-GGUF", "filename": "gemma-2-9b-it-q2_k.gguf", "name": "Gemma 2-9B IT"}, |
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{"repo_id": "Ffftdtd5dtft/gemma-2-27b-Q2_K-GGUF", "filename": "gemma-2-27b-q2_k.gguf", "name": "Gemma 2-27B"}, |
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{"repo_id": "Ffftdtd5dtft/Phi-3-mini-128k-instruct-Q2_K-GGUF", "filename": "phi-3-mini-128k-instruct-q2_k.gguf", "name": "Phi-3 Mini 128K Instruct"}, |
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{"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-q2_k.gguf", "name": "Meta Llama 3.1-8B"}, |
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{"repo_id": "Ffftdtd5dtft/Qwen2-7B-Instruct-Q2_K-GGUF", "filename": "qwen2-7b-instruct-q2_k.gguf", "name": "Qwen2 7B Instruct"}, |
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{"repo_id": "Ffftdtd5dtft/starcoder2-3b-Q2_K-GGUF", "filename": "starcoder2-3b-q2_k.gguf", "name": "Starcoder2 3B"}, |
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{"repo_id": "Ffftdtd5dtft/Qwen2-1.5B-Instruct-Q2_K-GGUF", "filename": "qwen2-1.5b-instruct-q2_k.gguf", "name": "Qwen2 1.5B Instruct"}, |
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{"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-70B-Q2_K-GGUF", "filename": "meta-llama-3.1-70b-q2_k.gguf", "name": "Meta Llama 3.1-70B"}, |
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{"repo_id": "Ffftdtd5dtft/Mistral-Nemo-Instruct-2407-Q2_K-GGUF", "filename": "mistral-nemo-instruct-2407-q2_k.gguf", "name": "Mistral Nemo Instruct 2407"}, |
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{"repo_id": "Ffftdtd5dtft/Hermes-3-Llama-3.1-8B-IQ1_S-GGUF", "filename": "hermes-3-llama-3.1-8b-iq1_s-imat.gguf", "name": "Hermes 3 Llama 3.1-8B"}, |
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{"repo_id": "Ffftdtd5dtft/Phi-3.5-mini-instruct-Q2_K-GGUF", "filename": "phi-3.5-mini-instruct-q2_k.gguf", "name": "Phi 3.5 Mini Instruct"}, |
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{"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-70B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-70b-instruct-q2_k.gguf", "name": "Meta Llama 3.1-70B Instruct"}, |
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{"repo_id": "Ffftdtd5dtft/codegemma-2b-IQ1_S-GGUF", "filename": "codegemma-2b-iq1_s-imat.gguf", "name": "Codegemma 2B"}, |
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{"repo_id": "Ffftdtd5dtft/Phi-3-mini-128k-instruct-IQ2_XXS-GGUF", "filename": "phi-3-mini-128k-instruct-iq2_xxs-imat.gguf", "name": "Phi 3 Mini 128K Instruct XXS"}, |
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{"repo_id": "Ffftdtd5dtft/TinyLlama-1.1B-Chat-v1.0-IQ1_S-GGUF", "filename": "tinyllama-1.1b-chat-v1.0-iq1_s-imat.gguf", "name": "TinyLlama 1.1B Chat"}, |
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{"repo_id": "Ffftdtd5dtft/Mistral-NeMo-Minitron-8B-Base-IQ1_S-GGUF", "filename": "mistral-nemo-minitron-8b-base-iq1_s-imat.gguf", "name": "Mistral NeMo Minitron 8B Base"}, |
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{"repo_id": "Ffftdtd5dtft/Mistral-Nemo-Instruct-2407-Q2_K-GGUF", "filename": "mistral-nemo-instruct-2407-q2_k.gguf", "name": "Mistral Nemo Instruct 2407"} |
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] |
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class ModelManager: |
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def __init__(self): |
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self.models = {} |
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def load_model(self, model_config): |
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if model_config['name'] not in self.models: |
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try: |
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self.models[model_config['name']] = Llama.from_pretrained(repo_id=model_config['repo_id'], filename=model_config['filename'], use_auth_token=HUGGINGFACE_TOKEN) |
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except Exception as e: |
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print(f"Error loading model {model_config['name']}: {e}") |
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def load_all_models(self): |
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with ThreadPoolExecutor() as executor: |
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for config in model_configs: |
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executor.submit(self.load_model, config) |
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return self.models |
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model_manager = ModelManager() |
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global_data['models'] = model_manager.load_all_models() |
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class ChatRequest(BaseModel): |
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message: str |
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def normalize_input(input_text): |
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return input_text.strip() |
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def remove_duplicates(text): |
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text = re.sub(r'(Hello there, how are you\? \[/INST\]){2,}', 'Hello there, how are you? [/INST]', text) |
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text = re.sub(r'(How are you\? \[/INST\]){2,}', 'How are you? [/INST]', text) |
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text = text.replace('[/INST]', '') |
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lines = text.split('\n') |
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unique_lines = [] |
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seen_lines = set() |
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for line in lines: |
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if line not in seen_lines: |
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unique_lines.append(line) |
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seen_lines.add(line) |
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return '\n'.join(unique_lines) |
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@GPU(duration=1) |
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def generate_model_response(model, inputs): |
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try: |
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response = model(inputs) |
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return remove_duplicates(response['choices'][0]['text']) |
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except Exception as e: |
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print(f"Error generating model response: {e}") |
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return "" |
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def remove_repetitive_responses(responses): |
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unique_responses = {} |
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for response in responses: |
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if response['model'] not in unique_responses: |
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unique_responses[response['model']] = response['response'] |
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return unique_responses |
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async def process_message(message): |
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inputs = normalize_input(message) |
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with ThreadPoolExecutor() as executor: |
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futures = [ |
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executor.submit(generate_model_response, model, inputs) |
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for model in global_data['models'].values() |
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] |
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responses = [{'model': model_name, 'response': future.result()} for model_name, future in zip(global_data['models'].keys(), as_completed(futures))] |
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unique_responses = remove_repetitive_responses(responses) |
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formatted_response = "" |
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for model, response in unique_responses.items(): |
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formatted_response += f"**{model}:**\n{response}\n\n" |
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curl_command = f""" |
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curl -X POST -H "Content-Type: application/json" \\ |
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-d '{{"message": "{message}"}}' \\ |
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http://localhost:7860/generate |
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""" |
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return formatted_response, curl_command |
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iface = gr.Interface( |
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fn=process_message, |
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inputs=gr.Textbox(lines=2, placeholder="Enter your message here..."), |
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outputs=[gr.Markdown(), gr.Textbox(label="cURL command")], |
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title="Multi-Model LLM API", |
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description="Enter a message and get responses from multiple LLMs.", |
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) |
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if __name__ == "__main__": |
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port = int(os.environ.get("PORT", 7860)) |
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iface.launch(server_port=port) |
