LiquidAI: LFM2.5-2.6B

{"about":"### 針對代理情境訓練的精簡模型\n\nLFM2.5-2.6B 是 Liquid AI 的文字模型，針對重視遵循指示、文件處理與連續多步驟執行的工作設計。在 LFM2.5 家族中，此版本另接受代理情境訓練：開發商讓它學習與工具及常見代理工作環境互動。這種訓練影響模型行為，應用程式中實際可執行的動作則由整合方式決定。\n\nLFM2.5-2.6B 以 LFM2 混合架構為基礎，含 26.9 億參數，結合短卷積區塊與分組注意力。Liquid AI 發展這個家族時著眼於裝置端運作，但在 Ailoot，模型透過文字介面使用：使用者傳入訊息，再取得文字答案。\n\n### 適用情境\n\n模型適合從文字擷取資訊、回答已提供資料的問題，以及處理長篇指示。例如，提供與客戶的往來訊息，先要求整理統一需求清單，再找出矛盾並準備澄清信。若需根據知識庫回答，可附上已找到的片段，並規定只能依據這些內容、保留名稱，以及註明所需事實缺漏的情況。\n\nLiquid AI 文件列出的工作方向包括資料擷取、RAG 與長上下文情境。開發商也明確限定適用範圍：代理式程式設計，以及未提供來源卻要求大量事實知識的任務，不是此版本的建議用途。處理產業術語時，宜在請求中加入定義與原始文件。\n\n### 上下文與語言\n\nAiloot 為此模型標示 128,000 個 token 的上下文，包含任務資料與對話歷史；答案大小應與這些內容一併規劃。開發商模型卡列出 16 種語言，包括俄語、英語、中文、德語與西班牙語。因此可將模型用於多語文件與往來訊息處理，並在指示中保留所需語言及結果格式。","alias_of":null,"aliases":[],"author":"Liquid","canonical_model":"liquid/lfm-2.5-2.6b","ctx":null,"formats":["chat","gemini","messages","responses"],"id":"liquid/lfm-2.5-2.6b","input":["text"],"kind":"text","max_out":null,"methods":[{"cache_read":0.08,"cache_write":null,"cache_write_1h":null,"cache_write_5m":null,"conditional_meter":null,"ctx":null,"family":"anthropic","fast_multiplier":1,"flex_multiplier":1,"fmt":"messages","http":"POST","img_in":null,"img_out":null,"in":0.7,"input":["text"],"label":"Anthropic Messages","max_out":null,"max_tool_calls":null,"out":1.0,"output":["text"],"path":"/v1/messages","request_model_id":"liquid/lfm-2.5-2.6b","route_label":"Ultra","status_reads":[],"tiers":[],"tool_prices":null,"unit":"token","units":[]},{"cache_read":0.08,"cache_write":null,"cache_write_1h":null,"cache_write_5m":null,"conditional_meter":null,"ctx":null,"family":"openai","fast_multiplier":1,"flex_multiplier":1,"fmt":"responses","http":"POST","img_in":null,"img_out":null,"in":0.7,"input":["text"],"label":"OpenAI Responses","max_out":null,"max_tool_calls":null,"out":1.0,"output":["text"],"path":"/v1/responses","request_model_id":"liquid/lfm-2.5-2.6b","route_label":"Ultra","status_reads":[],"tiers":[],"tool_prices":null,"unit":"token","units":[]},{"cache_read":0.08,"cache_write":null,"cache_write_1h":null,"cache_write_5m":null,"conditional_meter":null,"ctx":null,"family":"openai","fast_multiplier":1,"flex_multiplier":1,"fmt":"chat","http":"POST","img_in":null,"img_out":null,"in":0.7,"input":["text"],"label":"OpenAI Chat","max_out":null,"max_tool_calls":null,"out":1.0,"output":["text"],"path":"/v1/chat/completions","request_model_id":"liquid/lfm-2.5-2.6b","route_label":"Ultra","status_reads":[],"tiers":[],"tool_prices":null,"unit":"token","units":[]},{"cache_read":0.08,"cache_write":null,"cache_write_1h":null,"cache_write_5m":null,"conditional_meter":null,"ctx":null,"family":"gemini","fast_multiplier":1,"flex_multiplier":1,"fmt":"gemini","http":"POST","img_in":null,"img_out":null,"in":0.7,"input":["text"],"label":"Gemini generateContent","max_out":null,"max_tool_calls":null,"out":1.0,"output":["text"],"path":"/v1beta/models/liquid/lfm-2.5-2.6b:generateContent","request_model_id":"liquid/lfm-2.5-2.6b","route_label":"Ultra","status_reads":[],"tiers":[],"tool_prices":null,"unit":"token","units":[]}],"model_aliases":[{"canonical_model_id":"liquid/lfm-2.5-2.6b","request_model_id":"liquid/lfm-2.5-2.6b","route_label":"Ultra"}],"name":"LiquidAI: LFM2.5-2.6B","price":{"in":0.7,"in_label":null,"out":1.0,"out_label":null,"sort_in":0.7,"sort_out":1.0,"varies":false},"public_id":"liquid/lfm-2.5-2.6b","short":"Liquid AI 的精簡文字模型，用於資料擷取、根據文件回答問題，以及使用大型上下文的連續工作。"}