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| from fastapi import FastAPI, HTTPException | |
| from pydantic import BaseModel | |
| from llama_cpp import Llama | |
| from concurrent.futures import ThreadPoolExecutor, as_completed | |
| import uvicorn | |
| import huggingface_hub | |
| import re | |
| from dotenv import load_dotenv | |
| import spaces | |
| load_dotenv() | |
| app = FastAPI() | |
| global_data = { | |
| 'models': {}, | |
| 'tokens': { | |
| 'eos': 'eos_token', | |
| 'pad': 'pad_token', | |
| 'padding': 'padding_token', | |
| 'unk': 'unk_token', | |
| 'bos': 'bos_token', | |
| 'sep': 'sep_token', | |
| 'cls': 'cls_token', | |
| 'mask': 'mask_token' | |
| } | |
| } | |
| model_configs = [ | |
| {"repo_id": "Ffftdtd5dtft/gpt2-xl-Q2_K-GGUF", "filename": "gpt2-xl-q2_k.gguf", "name": "GPT-2 XL"}, | |
| {"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"}, | |
| {"repo_id": "Ffftdtd5dtft/gemma-2-9b-it-Q2_K-GGUF", "filename": "gemma-2-9b-it-q2_k.gguf", "name": "Gemma 2-9B IT"}, | |
| {"repo_id": "Ffftdtd5dtft/gemma-2-27b-Q2_K-GGUF", "filename": "gemma-2-27b-q2_k.gguf", "name": "Gemma 2-27B"}, | |
| {"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"}, | |
| {"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"}, | |
| {"repo_id": "Ffftdtd5dtft/Qwen2-7B-Instruct-Q2_K-GGUF", "filename": "qwen2-7b-instruct-q2_k.gguf", "name": "Qwen2 7B Instruct"}, | |
| {"repo_id": "Ffftdtd5dtft/starcoder2-3b-Q2_K-GGUF", "filename": "starcoder2-3b-q2_k.gguf", "name": "Starcoder2 3B"}, | |
| {"repo_id": "Ffftdtd5dtft/Qwen2-1.5B-Instruct-Q2_K-GGUF", "filename": "qwen2-1.5b-instruct-q2_k.gguf", "name": "Qwen2 1.5B Instruct"}, | |
| {"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"}, | |
| {"repo_id": "Ffftdtd5dtft/Mistral-Nemo-Instruct-2407-Q2_K-GGUF", "filename": "mistral-nemo-instruct-2407-q2_k.gguf", "name": "Mistral Nemo Instruct 2407"}, | |
| {"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"}, | |
| {"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"}, | |
| {"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"}, | |
| {"repo_id": "Ffftdtd5dtft/codegemma-2b-IQ1_S-GGUF", "filename": "codegemma-2b-iq1_s-imat.gguf", "name": "Codegemma 2B"}, | |
| {"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"}, | |
| {"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"}, | |
| {"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"}, | |
| {"repo_id": "Ffftdtd5dtft/Mistral-Nemo-Instruct-2407-Q2_K-GGUF", "filename": "mistral-nemo-instruct-2407-q2_k.gguf", "name": "Mistral Nemo Instruct 2407"} | |
| ] | |
| class ModelManager: | |
| def __init__(self): | |
| self.loaded = False | |
| def load_model(self, model_config): | |
| try: | |
| return {"model": Llama.from_pretrained(repo_id=model_config['repo_id'], filename=model_config['filename']), "name": model_config['name']} | |
| except Exception: | |
| pass | |
| def load_all_models(self): | |
| if self.loaded: | |
| return global_data['models'] | |
| try: | |
| with ThreadPoolExecutor() as executor: | |
| futures = [executor.submit(self.load_model, config) for config in model_configs] | |
| models = [] | |
| for future in as_completed(futures): | |
| model = future.result() | |
| if model: | |
| models.append(model) | |
| global_data['models'] = models | |
| self.loaded = True | |
| return models | |
| except Exception: | |
| pass | |
| model_manager = ModelManager() | |
| model_manager.load_all_models() | |
| class ChatRequest(BaseModel): | |
| message: str | |
| top_k: int = 50 | |
| top_p: float = 0.95 | |
| temperature: float = 0.7 | |
| def normalize_input(input_text): | |
| return input_text.strip() | |
| def remove_duplicates(text): | |
| text = re.sub(r'(Hello there, how are you\? \[/INST\]){2,}', 'Hello there, how are you? [/INST]', text) | |
| text = re.sub(r'(How are you\? \[/INST\]){2,}', 'How are you? [/INST]', text) | |
| text = text.replace('[/INST]', '') | |
| lines = text.split('\n') | |
| unique_lines = [] | |
| seen_lines = set() | |
| for line in lines: | |
| if line not in seen_lines: | |
| seen_lines.add(line) | |
| unique_lines.append(line) | |
| return '\n'.join(unique_lines) | |
| def remove_repetitive_responses(responses): | |
| seen = set() | |
| unique_responses = [] | |
| for response in responses: | |
| normalized_response = remove_duplicates(response['response']) | |
| if normalized_response not in seen: | |
| seen.add(normalized_response) | |
| unique_responses.append(response) | |
| return unique_responses | |
| def generate_chat_response(request, model_data): | |
| try: | |
| user_input = normalize_input(request.message) | |
| llm = model_data['model'] | |
| response = llm(user_input, top_k=request.top_k, top_p=request.top_p, temperature=request.temperature) | |
| return {"model": model_data['name'], "response": response} | |
| except Exception: | |
| pass | |
| async def generate(request: ChatRequest): | |
| try: | |
| responses = [] | |
| with ThreadPoolExecutor() as executor: | |
| futures = [executor.submit(generate_chat_response, request, model_data) for model_data in global_data['models']] | |
| for future in as_completed(futures): | |
| try: | |
| response = future.result() | |
| if response: | |
| responses.append(response) | |
| except Exception: | |
| pass | |
| if not responses: | |
| raise HTTPException(status_code=500, detail="Error: No responses generated.") | |
| responses = remove_repetitive_responses(responses) | |
| best_response = responses[0] if responses else {} | |
| return { | |
| "best_response": best_response, | |
| "all_responses": responses | |
| } | |
| except Exception: | |
| pass | |
| if __name__ == "__main__": | |
| uvicorn.run(app, host="0.0.0.0", port=7860) | |