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| from pydantic import BaseModel | |
| from llama_cpp import Llama | |
| from concurrent.futures import ThreadPoolExecutor, as_completed | |
| import re | |
| import httpx | |
| import asyncio | |
| import gradio as gr | |
| import os | |
| from dotenv import load_dotenv | |
| import spaces | |
| import requests | |
| import random | |
| from faker import Faker | |
| load_dotenv() | |
| HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN") | |
| 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', | |
| 'n_ctx': 'n_ctx_token', | |
| 'vocab_size': 'vocab_size_token', | |
| 'n_embd': 'n_embd_token', | |
| 'n_head': 'n_head_token', | |
| 'n_layer': 'n_layer_token', | |
| 'n_vocab': 'n_vocab_token', | |
| 'block_size': 'block_size_token', | |
| 'n_rot': 'n_rot_token', | |
| 'rope_dim': 'rope_dim_token', | |
| 'rope_scaling': 'rope_scaling_token', | |
| 'n_positions': 'n_positions_token', | |
| 'use_cache': 'use_cache_token', | |
| 'use_parallel_inference': 'use_parallel_inference_token', | |
| 'parallel_inference_count': 'parallel_inference_count_token', | |
| 'use_mlock': 'use_mlock_token', | |
| 'use_mmap': 'use_mmap_token', | |
| 'use_cpu': 'use_cpu_token', | |
| 'f16_kv': 'f16_kv_token', | |
| 'f16_quant': 'f16_quant_token', | |
| 'f16_output': 'f16_output_token', | |
| 'use_flash_attn': 'use_flash_attn_token', | |
| 'max_seq_len': 'max_seq_len_token', | |
| 'do_sample': 'do_sample_token', | |
| 'top_k': 'top_k_token', | |
| 'top_p': 'top_p_token', | |
| 'temperature': 'temperature_token', | |
| 'num_return_sequences': 'num_return_sequences_token', | |
| 'use_repetition_penalty': 'use_repetition_penalty_token', | |
| 'repetition_penalty': 'repetition_penalty_token', | |
| 'no_repeat_ngram_size': 'no_repeat_ngram_size_token', | |
| 'bad_words_ids': 'bad_words_ids_token', | |
| 'use_token_logging': 'use_token_logging_token', | |
| 'use_tensor_parallel': 'use_tensor_parallel_token', | |
| 'tensor_parallel_size': 'tensor_parallel_size_token', | |
| 'use_gpu_memory_growth': 'use_gpu_memory_growth_token', | |
| 'use_multi_gpu_inference': 'use_multi_gpu_inference_token', | |
| 'multi_gpu_inference_count': 'multi_gpu_inference_count_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.models = {} | |
| def load_model(self, model_config): | |
| if model_config['name'] not in self.models: | |
| try: | |
| print(f"Loading model {model_config['name']}...") | |
| self.models[model_config['name']] = Llama.from_pretrained( | |
| repo_id=model_config['repo_id'], | |
| filename=model_config['filename'], | |
| use_auth_token=HUGGINGFACE_TOKEN | |
| ) | |
| print(f"Model {model_config['name']} loaded successfully.") | |
| except Exception as e: | |
| print(f"Error loading model {model_config['name']}: {e}") | |
| def load_all_models(self): | |
| with ThreadPoolExecutor() as executor: | |
| for config in model_configs: | |
| executor.submit(self.load_model, config) | |
| return self.models | |
| model_manager = ModelManager() | |
| global_data['models'] = model_manager.load_all_models() | |
| class ChatRequest(BaseModel): | |
| message: str | |
| 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: | |
| unique_lines.append(line) | |
| seen_lines.add(line) | |
| return '\n'.join(unique_lines) | |
| PROXY_URL = "https://uhhy-fsfsfs.hf.space/valid" | |
| def get_random_proxy(): | |
| try: | |
| response = requests.get(PROXY_URL) | |
| proxies = response.text.splitlines() | |
| return random.choice(proxies) | |
| except Exception as e: | |
| print(f"Error fetching proxy: {e}") | |
| return None | |
| fake = Faker() | |
| def generate_fake_ip(): | |
| return fake.ipv4() | |
| def generate_model_response(model, inputs): | |
| try: | |
| print(f"Generating response for model: {model}") | |
| response = model(inputs) | |
| print(f"Response from {model}: {response}") | |
| return remove_duplicates(response['choices'][0]['text']) | |
| except Exception as e: | |
| print(f"Error generating model response from {model}: {e}") | |
| return "Error generating response. Please try again later." | |
| def remove_repetitive_responses(responses): | |
| unique_responses = {} | |
| for response in responses: | |
| if response not in unique_responses: | |
| unique_responses[response] = response | |
| return unique_responses | |
| async def process_message(message): | |
| inputs = normalize_input(message) | |
| with ThreadPoolExecutor() as executor: | |
| futures = [ | |
| executor.submit(generate_model_response, model, inputs) | |
| for model in global_data['models'].values() | |
| ] | |
| responses = [] | |
| for future in as_completed(futures): | |
| try: | |
| response = future.result() | |
| responses.append(response) | |
| except Exception as e: | |
| print(f"Error with model: {e}") | |
| responses.append("Error generating response. Please try again later.") | |
| unique_responses = remove_repetitive_responses(responses) | |
| formatted_response = "" | |
| for model, response in unique_responses.items(): | |
| formatted_response += f"**{model}:**\n{response}\n\n" | |
| curl_command = f""" | |
| curl -X POST -H "Content-Type: application/json" \\ | |
| -d '{{"message": "{message}"}}' \\ | |
| http://localhost:7860/generate | |
| """ | |
| return formatted_response, curl_command | |
| iface = gr.Interface( | |
| fn=process_message, | |
| inputs=gr.Textbox(lines=2, placeholder="Enter your message here..."), | |
| outputs=[gr.Markdown(), gr.Textbox(label="cURL command")], | |
| title="Multi-Model LLM API", | |
| description="Enter a message and get responses from multiple LLMs.", | |
| ) | |
| if __name__ == "__main__": | |
| port = int(os.environ.get("PORT", 7860)) | |
| iface.launch(server_port=port) |