overfit-add-llm

Add a local LLM to a .NET app with Overfit

Wires Overfit — a pure-C#/.NET, in-process LLM runtime — into an existing project so it loads a GGUF model and runs on the CPU. No Python, no model server, no data egress.

New project? Use the template instead: dotnet new install DevOnBike.Overfit.Templates → dotnet new overfit-chat. This skill is for adding Overfit to an existing codebase.

Instructions

1. Identify the target. Find the .csproj to add inference to and classify it: web / DI host (ASP.NET, Minimal API — has WebApplication.CreateBuilder) vs console / library (direct calls). Overfit targets net10.0; if the project is older, say so and confirm before bumping the TFM.

2. Add the packages.

   dotnet add package DevOnBike.Overfit                 # the runtime
   dotnet add package DevOnBike.Overfit.Extensions.AI   # only if you want IChatClient / DI / Semantic Kernel
   

   Add Microsoft.Extensions.AI too if the app will call builder.Services.AddChatClient(...).

3. Get a model (GGUF). Check for an existing one first: a *.gguf in the repo, an OVERFIT_MODEL_DIR env var, or ask the user for a path. If there is none, download a small one to test with:

   curl -L -o model.gguf https://huggingface.co/bartowski/Qwen2.5-0.5B-Instruct-GGUF/resolve/main/Qwen2.5-0.5B-Instruct-Q4_K_M.gguf
   

   Then gitignore it — add *.gguf to .gitignore (models are large; never commit them). Size guidance: a 0.5B–1B Q4_K is a good default; only go to 3B+ if the box has the RAM (mmap keeps the managed heap tiny, but the OS still pages the weights).

4. Wire it. Pick the path that matches the project:

   • Web / DI — as a standard IChatClient (drops into the .NET AI template & Semantic Kernel):

     using DevOnBike.Overfit.LanguageModels;
     using DevOnBike.Overfit.Extensions.AI;
     using Microsoft.Extensions.AI;
     
     var overfit = OverfitClient.LoadGguf(builder.Configuration["ModelPath"] ?? "model.gguf", mmap: true);
     builder.Services.AddSingleton(overfit);
     builder.Services.AddChatClient(overfit.AsChatClient());   // inject IChatClient anywhere
     

     Stream in an endpoint via IChatClient.GetStreamingResponseAsync(prompt) (update.Text per token).

   • Console / library — direct:

     using DevOnBike.Overfit.LanguageModels;
     
     using var client = OverfitClient.LoadGguf("model.gguf", mmap: true);
     Console.WriteLine(client.Send("Say hello in one sentence."));
     

5. Verify end-to-end. dotnet build, then actually run it and send one prompt — confirm tokens come back (don't stop at "it compiles"). For a web app, hit the endpoint; for a console app, run it.

6. Report the wiring + the model path, and point at the follow-ons the user is likely to want next (below). Leave the tree clean/staged — do not commit (that's the user's call).

Follow-ons (mention, don't build unless asked)

• RAG: SentenceEmbedder.ForMiniLm(dir).AsEmbeddingGenerator() → a standard IEmbeddingGenerator; pair with Overfit's in-process VectorStore.
• OpenAI-compatible server (no code): dotnet tool install -g DevOnBike.Overfit.Cli then overfit serve model.gguf → point any OpenAI client at http://localhost:8080/v1.
• Streaming, tool calling, guaranteed JSON, samplers (top-nσ / DRY): via ChatSession / SamplingOptions.

Rules

• Never commit model files (*.gguf). Add them to .gitignore.
• Prefer mmap: true for GGUF — low, reclaimable working set instead of an F32 blow-up.
• License: Overfit is AGPL-3.0-or-later / commercial — flag this if the target is a closed-source or distributed product (they may need the commercial license: [email protected]).
• Don't fabricate the model path — use an existing model, an env var, or download one; ask if unsure.
• Do the git work as the user's action: stop at a built, verified, staged state and report.