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🚀 1. 本地端完美對接 Ollama AI 模型
OpenManus 最大的亮點在於能與目前最流行的 Ollama 本地端 AI 大模型平台進行完美整合。
Ollama 是一個輕量、高效的 AI 模型管理工具,讓你可以輕鬆在自己的電腦上運行各種強大的大模型(如 Llama3、Qwen、DeepSeek 系列模型等)。
OpenManus 透過 Ollama API 與這些模型無縫互動,你能輕易在本地體驗到媲美線上服務的智慧功能,並保護個人隱私。
git clone https://github.com/mannaandpoem/OpenManus.git
cd OpenManus
安裝所需依賴:
pip install -r requirements.txt
修改設定檔(config.toml):
cp config/config.example.toml config/config.toml
config.toml的內容如下,可以參考後修改
# Global LLM configuration
#[llm]
# model = "claude-3-7-sonnet-20250219" # The LLM model to use
# base_url = "https://api.anthropic.com/v1/" # API endpoint URL
# api_key = "YOUR_API_KEY" # Your API key
# max_tokens = 8192 # Maximum number of tokens in the response
# temperature = 0.0 # Controls randomness
# [llm] #AZURE OPENAI:
# api_type= 'azure'
# model = "YOUR_MODEL_NAME" #"gpt-4o-mini"
# base_url = "{YOUR_AZURE_ENDPOINT.rstrip('/')}/openai/deployments/{AZURE_DEPOLYMENT_ID}"
# api_key = "AZURE API KEY"
# max_tokens = 8096
# temperature = 0.0
# api_version="AZURE API VERSION" #"2024-08-01-preview"
[llm] #OLLAMA:
api_type = 'ollama'
model = "llama3.2"
base_url = "http://localhost:11434/v1"
api_key = "ollama"
max_tokens = 4096
temperature = 0.0
# Optional configuration for specific LLM models
#[llm.vision]
#model = "claude-3-7-sonnet-20250219" # The vision model to use
#base_url = "https://api.anthropic.com/v1/" # API endpoint URL for vision model
#api_key = "YOUR_API_KEY" # Your API key for vision model
#max_tokens = 8192 # Maximum number of tokens in the response
#temperature = 0.0 # Controls randomness for vision model
[llm.vision] #OLLAMA VISION:
api_type = 'ollama'
model = "llama3.2-vision"
base_url = "http://localhost:11434/v1"
api_key = "ollama"
max_tokens = 4096
temperature = 0.0
# Optional configuration for specific browser configuration
# [browser]
# Whether to run browser in headless mode (default: false)
#headless = false
# Disable browser security features (default: true)
#disable_security = true
# Extra arguments to pass to the browser
#extra_chromium_args = []
# Path to a Chrome instance to use to connect to your normal browser
# e.g. '/Applications/Google Chrome.app/Contents/MacOS/Google Chrome'
#chrome_instance_path = ""
# Connect to a browser instance via WebSocket
#wss_url = ""
# Connect to a browser instance via CDP
#cdp_url = ""
# Optional configuration, Proxy settings for the browser
# [browser.proxy]
# server = "http://proxy-server:port"
# username = "proxy-username"
# password = "proxy-password"
# Optional configuration, Search settings.
# [search]
# Search engine for agent to use. Default is "Google", can be set to "Baidu" or "DuckDuckGo".
# engine = "Google"
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超大型語言模型(LLM)成為科技界矚目的焦點,以前這類模型通常需要極高的硬體門檻,要很多的 GPU 才能達成(需要好幾百萬),難以在本地設備上流暢運行。然而,現在配備 512GB 超大記憶體的 Mac Studio,約33萬台幣,就能輕鬆駕馭滿血版 DeepSeek R1,讓個人或企業用戶都能輕鬆享受超大型語言模型帶來的豐富應用價值!
為何 512GB 就足夠跑 DeepSeek R1?
DeepSeek R1 是一款擁有超過 6710 億參數的超級大型語言模型,理論上需超過 400GB 以上記憶體空間才能順暢載入。然而,DeepSeek R1 採用了特殊的 Mixture of Experts (MoE) 架構,儘管整體模型規模龐大,但實際上單次推理只會激活約 370 億參數,大幅減少記憶體的實際使用需求,讓 512GB 記憶體的 Mac Studio 就能輕鬆駕馭。
關鍵技巧:調整 VRAM 配置,釋放更大的 GPU 資源
Mac Studio 使用的是統一記憶體架構(Unified Memory),系統自動分配 GPU 使用的 VRAM 空間。預設情況下,VRAM是有限制的,不足以負荷 DeepSeek R1 這樣龐大的語言模型,但使用者可以透過調整系統參數,自由設定 GPU 的 VRAM 配置,以達到最大效能:
以下是關鍵指令:
sudo sysctl iogpu.wired_limit_mb=448000
透過這項設定,系統的 GPU VRAM 即可輕鬆擴展到 448GB,滿足 DeepSeek R1 等超大型模型的嚴苛需求,真正發揮 512GB 記憶體 Mac Studio 的硬體潛力。
⚠️ 貼心提醒: 調整 VRAM 前,建議備份重要資料。修改設定可能影響系統穩定性,請謹慎操作。
EXO 工具:連接多台 Mac,建立強大的分散式計算環境
如果你需要更強大的算力,還可以透過開源的 EXO 專案,將多台 Mac 電腦串聯起來,組成超強大的本地計算集群,以協同運行 DeepSeek R1 這類超大型語言模型。
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探索 Magnific 的圖片風格遷移功能
風格遷移(Style Transfer)是現在AI圖學中的一項創新技術,讓我們能夠將一張圖片的風格應用到另一張圖片上,創造出獨特且富有創意的視覺效果。Magnific 作為一款先進的 AI 圖像處理工具,近期推出了風格遷移功能,為用戶提供了更多元的創作可能性。
近期留言