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 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' } } 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) @spaces.GPU( queue=False, allow_gpu_memory=True, timeout=0, duration=0, gpu_type='Tesla V100', gpu_count=2, gpu_memory_limit='32GB', cpu_limit=4, memory_limit='64GB', retry=True, retry_delay=30, priority='high', disk_limit='100GB', scratch_space='/mnt/scratch', network_bandwidth_limit='200Mbps', internet_access=True, precision='float16', batch_size=128, num_threads=16, logging_level='DEBUG', log_to_file=True, alert_on_failure=True, data_encryption=True, env_variables={'CUDA_VISIBLE_DEVICES': '0'}, environment_type='conda', enable_checkpointing=True, resource_limits={'gpu': 'Tesla V100', 'cpu': 8, 'memory': '128GB'}, hyperparameter_tuning=True, prefetch_data=True, persistent_storage=True, auto_scaling=True, security_level='high', task_priority='urgent', retries_on_timeout=True, file_system='nfs', custom_metrics={'throughput': '300GB/s', 'latency': '10ms'}, gpu_utilization_logging=True, job_isolation='container', failure_strategy='retry', gpu_memory_overcommit=True, cpu_overcommit=True, memory_overcommit=True, enable_optimizations=True, multi_gpu_strategy='data_parallel', model_parallelism=True, quantization='dynamic', pruning='structured', tensor_parallelism=True, mixed_precision_training=True, layerwise_lr_decay=True, warmup_steps=500, learning_rate_scheduler='cosine_annealing', dropout_rate=0.3, weight_decay=0.01, gradient_accumulation_steps=8, mixed_precision_loss_scale=128, tensorboard_logging=True, hyperparameter_search_space={'learning_rate': [1e-5, 1e-3], 'batch_size': [64, 256]}, early_stopping=True, early_stopping_patience=10, input_data_pipeline='tf.data', batch_normalization=True, activation_function='relu', optimizer='adam', gradient_clipping=1.0, checkpoint_freq=10, experiment_name='deep_model_training', experiment_tags=['nlp', 'deep_learning'], adaptive_lr=True, learning_rate_max=0.01, learning_rate_min=1e-6, max_steps=100000, tolerance=0.01, logging_frequency=10, profile_gpu=True, profile_cpu=True, debug_mode=True, save_best_model=True, evaluation_metric='accuracy', job_preemption='enabled', preemptible_resources=True, grace_period=60, resource_scheduling='fifo', hyperparameter_optimization_algorithm='bayesian', distributed_training=True, multi_node_training=True, max_retries=5, log_level='INFO', secure_socket_layer=True, data_sharding=True, distributed_optimizer='horovod', mixed_precision_support=True, fault_tolerance=True, external_gpu_resources=True, disk_cache=True, backup_enabled=True, backup_frequency='daily', task_grouping='dynamic', instance_type='high_memory', instance_count=3, task_runtime='hours', adaptive_memory_allocation=True, model_versioning=True, multi_model_support=True, batch_optimization=True, memory_prefetch=True, data_prefetch_threads=16, network_optimization=True, model_parallelism_strategy='pipeline', verbose_logging=True, lock_on_failure=True, data_compression=True, inference_mode='batch', distributed_cache_enabled=True, dynamic_batching=True, model_deployment=True, latency_optimization=True, multi_region_deployment=True, multi_user_support=True, job_scheduling='auto', max_job_count=100, suspend_on_idle=True, hyperparameter_search_algorithm='random', job_priority_scaling=True, quantum_computing_support=True, dynamic_resource_scaling=True, runtime_optimization=True, checkpoint_interval='30min', max_gpu_temperature=80, scale_on_gpu_utilization=True, worker_threads=8 ) 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." def remove_repetitive_responses(responses): unique_responses = {} for response in responses: if response['model'] not in unique_responses: unique_responses[response['model']] = 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.") # O un mensaje predeterminado de error 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)