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import os |
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import gc |
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import psutil |
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import cachetools |
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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 torch |
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from dotenv import load_dotenv |
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from fastapi import FastAPI |
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from fastapi.responses import JSONResponse |
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import uvicorn |
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from threading import Thread |
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import gptcache |
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from sklearn.metrics.pairwise import cosine_similarity |
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from sklearn.feature_extraction.text import TfidfVectorizer |
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load_dotenv() |
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HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN") |
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cache = cachetools.TTLCache(maxsize=100, ttl=60) |
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global_data = { |
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'models': {}, |
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'tokensxx': { |
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'eos': '<|end_of-text|>', |
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'pad': '<pad>', |
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'padding': '<pad>', |
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'unk': '<unk>', |
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'bos': '<|begin_of_text|>', |
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'sep': '<|sep|>', |
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'cls': '<|cls|>', |
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'mask': '<mask>', |
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'eot': '<|eot_id|>', |
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'eom': '<|eom_id|>', |
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'lf': '<|0x0A|>' |
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}, |
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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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'model_metadata': {}, |
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'eos': {}, |
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'pad': {}, |
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'padding': {}, |
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'unk': {}, |
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'bos': {}, |
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'sep': {}, |
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'cls': {}, |
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'mask': {}, |
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'eot': {}, |
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'eom': {}, |
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'lf': {}, |
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'max_tokens': {}, |
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'tokenizers': {}, |
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'model_params': {}, |
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'model_size': {}, |
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'model_ftype': {}, |
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'n_ctx_train': {}, |
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'n_embd': {}, |
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'n_layer': {}, |
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'n_head': {}, |
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'n_head_kv': {}, |
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'n_rot': {}, |
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'n_swa': {}, |
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'n_embd_head_k': {}, |
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'n_embd_head_v': {}, |
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'n_gqa': {}, |
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'n_embd_k_gqa': {}, |
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'n_embd_v_gqa': {}, |
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'f_norm_eps': {}, |
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'f_norm_rms_eps': {}, |
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'f_clamp_kqv': {}, |
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'f_max_alibi_bias': {}, |
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'f_logit_scale': {}, |
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'n_ff': {}, |
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'n_expert': {}, |
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'n_expert_used': {}, |
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'causal_attn': {}, |
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'pooling_type': {}, |
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'rope_type': {}, |
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'rope_scaling': {}, |
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'freq_base_train': {}, |
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'freq_scale_train': {}, |
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'n_ctx_orig_yarn': {}, |
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'rope_finetuned': {}, |
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'ssm_d_conv': {}, |
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'ssm_d_inner': {}, |
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'ssm_d_state': {}, |
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'ssm_dt_rank': {}, |
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'ssm_dt_b_c_rms': {}, |
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'vocab_type': {}, |
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'model_type': {} |
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} |
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model_configs = [ |
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{"repo_id": "Hjgugugjhuhjggg/testing_semifinal-Q2_K-GGUF", "filename": "testing_semifinal-q2_k.gguf", "name": "testing"}, |
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{"repo_id": "bartowski/Llama-3.2-3B-Instruct-uncensored-GGUF", "filename": "Llama-3.2-3B-Instruct-uncensored-Q2_K.gguf", "name": "Llama-3.2-3B-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": "bartowski/QwQ-32B-Preview-GGUF", "filename": "QwQ-32B-Preview-Q2_K.gguf", "name": "QwQ-32B-Preview"}, |
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{"repo_id": "Ffftdtd5dtft/Codestral-22B-v0.1-Q2_K-GGUF", "filename": "codestral-22b-v0.1-q2_k.gguf", "name": "Codestral-22B-v0.1"}, |
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{"repo_id": "Ffftdtd5dtft/WizardLM-13B-Uncensored-Q2_K-GGUF", "filename": "wizardlm-13b-uncensored-q2_k.gguf", "name": "WizardLM-13B-Uncensored"}, |
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{"repo_id": "Ffftdtd5dtft/Qwen2-Math-72B-Instruct-Q2_K-GGUF", "filename": "qwen2-math-72b-instruct-q2_k.gguf", "name": "Qwen2-Math-72B-Instruct"}, |
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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"}, |
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{"repo_id": "Ffftdtd5dtft/DeepSeek-Coder-V2-Lite-Instruct-Q2_K-GGUF", "filename": "deepseek-coder-v2-lite-instruct-q2_k.gguf", "name": "DeepSeek-Coder-V2-Lite"}, |
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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( |
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repo_id=model_config['repo_id'], |
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filename=model_config['filename'], |
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use_auth_token=HUGGINGFACE_TOKEN, |
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n_threads=20, |
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use_gpu=False |
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) |
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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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def release_resources(): |
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try: |
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if torch.cuda.is_available(): |
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torch.cuda.empty_cache() |
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gc.collect() |
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except Exception as e: |
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print(f"Failed to release resources: {e}") |
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def resource_manager(): |
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MAX_RAM_PERCENT = 1 |
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MAX_CPU_PERCENT = 1 |
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MAX_GPU_PERCENT = 1 |
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while True: |
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try: |
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virtual_mem = psutil.virtual_memory() |
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current_ram_percent = virtual_mem.percent |
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if current_ram_percent > MAX_RAM_PERCENT: |
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release_resources() |
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current_cpu_percent = psutil.cpu_percent() |
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if current_cpu_percent > MAX_CPU_PERCENT: |
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psutil.Process(os.getpid()).nice() |
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if torch.cuda.is_available(): |
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gpu = torch.cuda.current_device() |
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gpu_mem = torch.cuda.memory_allocated(gpu) / (1024 * 1024) |
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total_gpu_mem = torch.cuda.get_device_properties(gpu).total_memory / (1024 * 1024) |
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gpu_mem_percent = (gpu_mem / total_gpu_mem) * 100 |
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if gpu_mem_percent > MAX_GPU_PERCENT: |
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release_resources() |
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except Exception as e: |
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print(f"Error in resource manager: {e}") |
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def run_resource_manager(): |
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resource_manager() |
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Thread(target=run_resource_manager, daemon=True).start() |
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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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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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def get_best_response(responses): |
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vectorizer = TfidfVectorizer().fit_transform(responses) |
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similarity_matrix = cosine_similarity(vectorizer) |
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total_similarities = similarity_matrix.sum(axis=1) |
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best_response_index = total_similarities.argmax() |
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return responses[best_response_index] |
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async def generate_model_response(model, inputs, max_tokens=2048): |
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try: |
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response = model(inputs) |
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text = remove_duplicates(response['choices'][0]['text']) |
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if len(text.split()) > max_tokens: |
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return text[:max_tokens] |
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return text |
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except Exception as e: |
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return "" |
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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 = [ |
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future.result() |
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for future in as_completed(futures) |
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] |
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best_response = get_best_response(responses) |
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return best_response |
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app = FastAPI() |
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@app.post("/generate") |
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async def generate(request: BaseModel): |
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try: |
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response = await process_message(request.message) |
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return JSONResponse(content={"response": response}) |
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except Exception as e: |
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return JSONResponse(content={"error": str(e)}) |
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def run_uvicorn(): |
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uvicorn.run(app, host="0.0.0.0", port=7860) |
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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(), |
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title="Multi-Model LLM API (CPU Optimized)", |
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description="" |
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) |
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def run_gradio(): |
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iface.launch(server_port=7862, prevent_thread_lock=True) |
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if __name__ == "__main__": |
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Thread(target=run_uvicorn).start() |
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Thread(target=run_gradio).start() |
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asyncio.get_event_loop().run_forever() |
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