diff --git a/lua/custom/plugins/llm.lua b/lua/custom/plugins/llm.lua
new file mode 100644
index 00000000..916fc66c
--- /dev/null
+++ b/lua/custom/plugins/llm.lua
@@ -0,0 +1,48 @@
+return {
+  {
+    'milanglacier/minuet-ai.nvim',
+    config = function()
+      require('minuet').setup {
+        virtualtext = {
+          auto_trigger_ft = {},
+          keymap = {
+            -- accept whole completion
+            accept = '<A-A>',
+            -- accept one line
+            accept_line = '<A-a>',
+            -- accept n lines (prompts for number)
+            -- e.g. "A-z 2 CR" will accept 2 lines
+            accept_n_lines = '<A-z>',
+            -- Cycle to prev completion item, or manually invoke completion
+            prev = '<A-[>',
+            -- Cycle to next completion item, or manually invoke completion
+            next = '<A-]>',
+            dismiss = '<A-e>',
+          },
+        },
+        provider = 'openai_fim_compatible',
+        n_completions = 1, -- recommend for local model for resource saving
+        -- I recommend beginning with a small context window size and incrementally
+        -- expanding it, depending on your local computing power. A context window
+        -- of 512, serves as an good starting point to estimate your computing
+        -- power. Once you have a reliable estimate of your local computing power,
+        -- you should adjust the context window to a larger value.
+        context_window = 512,
+        provider_options = {
+          openai_fim_compatible = {
+            -- For Windows users, TERM may not be present in environment variables.
+            -- Consider using APPDATA instead.
+            api_key = 'TERM',
+            name = 'Ollama',
+            end_point = 'http://localhost:11434/v1/completions',
+            model = 'qwen2.5-coder:7b',
+            optional = {
+              max_tokens = 56,
+              top_p = 0.9,
+            },
+          },
+        },
+      }
+    end,
+  },
+}