Local model management ​

Managing which LLM the agent loop REPL talks to - switching mid-session, auto-routing, and running local model servers.

Switching models ​

/model <name> switches models mid-session. For local backends (file, gguf), the REPL downloads and starts the server if it isn't already running, then shows a progress display until the completions endpoint is accepting requests - the first prompt after the switch never gets a "network error" while weights load.

/model qwen3.5-4b                     # switch to a known alias
/model default qwen3.5-4b             # save as default startup model
/model default                        # show the current default

The default model is persisted to ~/.kdeps/agent-loop-settings.yaml and loaded automatically at startup when --model is not passed.

Same alias, different backends ​

llamafile, GGUF, and Ollama registries can each have their own, unrelated entry under the same bare alias (e.g. qwen3.5 might exist as a distinct download in both the llamafile and GGUF catalogs, or as a locally-pulled Ollama tag). When kdeps detects this, /model list and /model <tab> show every colliding entry qualified by its backend instead of hiding one:

llamafile:qwen3.5
gguf:qwen3.5

Switch to a specific one with its qualified name:

/model gguf:qwen3.5

Typing the bare, still-ambiguous name auto-picks the same backend kdeps has always preferred (ollama > gguf > llamafile) and prints a one-line notice so you know which one you got:

/model qwen3.5
"qwen3.5" is ambiguous across backends (llamafile:qwen3.5, gguf:qwen3.5) -- using gguf:qwen3.5. Use the full name to pick a specific one.

Non-colliding names - the vast majority of aliases - are completely unaffected; you only ever need the backend: prefix when kdeps tells you to.

--model auto: route across your configured models ​

--model auto (or KDEPS_AGENT_MODEL=auto) picks the best hardware-fit model from your own llm.models config (~/.kdeps/config.yaml) via llmfit - the same auto strategy the workflow-mode router uses, so one config drives both modes:

yaml
# ~/.kdeps/config.yaml
llm:
  strategy: auto
  models:
    - model: llama3.2:1b
      backend: file
    - model: qwen2.5:7b
      backend: gguf
bash
kdeps --model auto

If nothing's configured (or none of it scores), auto falls through to the same installed-model pick described below, then the same fixed tiers - it's always at least as good as omitting --model entirely.

--model auto-router: zero-config, fully automatic ​

--model auto still scores your configured llm.models. --model auto-router (or KDEPS_AGENT_MODEL=auto-router) skips llm.models entirely, every time, and always goes straight to discovery:

bash
kdeps --model auto-router
  1. Best-fit installed local model - every cached llamafile, loadable GGUF, and pulled Ollama tag scored via llmfit. Requires llmfit on PATH; skipped (no cost) when it isn't installed.
  2. Cloud fallback - the first provider with both an API key env var set and a known representative model (gpt-4o for OpenAI, claude-sonnet-4-6 for Anthropic, ...).
  3. Fixed tiers - if neither finds anything, falls through to the same fixed-order pick described below.

Workflow mode has the same sentinel via model: auto-router on a chat resource - see LLM backends.

How a model is picked when none is configured ​

With no --model flag, no saved default, and no model: in ~/.kdeps/config.yaml, kdeps first checks whether llmfit is installed (brew install AlexsJones/llmfit/llmfit): if it is, and at least one local model (llamafile/GGUF/Ollama) is already downloaded, kdeps starts with whichever downloaded model llmfit scores as the best hardware fit for this machine, skipping the fixed order below entirely.

Otherwise - no llmfit, or nothing downloaded yet - kdeps picks the first option that is actually usable, in this fixed order:

  1. llamafile - the llamafile runner binary on PATH, or a cached *.llamafile in the models directory (a .llamafile is self-executing, so no runner is needed).
  2. GGUF - the first *.gguf in the models directory that llama-server can load. Files with an unreadable header or a GGUFv1 container are skipped: current llama.cpp builds refuse them (GGUFv1 is no longer supported), so serving one would start a server that exits immediately and fail every request.
  3. Cloud - the first known provider whose API key env var is set (OPENAI_API_KEY, ANTHROPIC_API_KEY, ...).
  4. ollama - the ollama binary on PATH.

The models directory is $KDEPS_MODELS_DIR, or ~/.kdeps/models when that is unset. If nothing matches, kdeps starts with no model and /model lists what you can download.

Each local model server writes its stdout and stderr next to the model file as <model>.server.log. When a server fails to become healthy, the tail of that log is included in the error, so a bad model file reports its real cause instead of a bare connection failure.

Registering a model by URL ​

/model <url> registers a custom model and switches to it. The URL kind is detected automatically:

bash
# Direct GGUF or llamafile file - downloaded immediately, then served locally
/model https://huggingface.co/user/repo/resolve/main/Qwen2.5-7B-Q4_K_M.gguf
/model https://example.com/rocket-3b.Q4_K_M.llamafile

# Any other http(s) URL is treated as an OpenAI-compatible endpoint
/model http://localhost:1234/v1          # LM Studio / llama.cpp server
/model https://api.together.xyz/v1       # a hosted compat provider

Each registered model gets a memorable, kind-prefixed ID so it's easy to recall and retype next time:

  • .gguf URL -> gguf-<filename> (e.g. gguf-Qwen2.5-7B-Q4_K_M)
  • .llamafile URL -> llamafile-<filename>
  • OpenAI-compatible endpoint -> api-<host> (e.g. api-localhost-1234)

A collision with an existing model gets the next free -2, -3, ... suffix, so re-registering never overwrites.

Registered models persist and keep appearing in /model and /model <tab>:

  • .gguf / .llamafile URLs are added to ~/.kdeps/gguf_versions.yaml / llamafile_versions.yaml (downloaded on registration).
  • OpenAI-compatible endpoints are saved to ~/.kdeps/agent-loop-settings.yaml. No API key is stored; if the endpoint needs one, set OPENAI_API_KEY (or KDEPS_CUSTOM_API_KEY) in your environment.

Favorite models ​

Star models you use often so they lead the /model and /model <tab> lists and persist across sessions:

bash
/model favorite gpt-4o          # star it (also: /model fav, /model star)
/model favorite gguf-my-model
/model unfavorite gpt-4o        # remove the star (also: /model unfav)

Favorites are saved to ~/.kdeps/agent-loop-settings.yaml, shown first (marked ★) with no text typed, and remain selectable even if the model is a cloud model or a not-yet-downloaded alias.

Searching and downloading from HuggingFace ​

/model hff lets you discover and download GGUF models directly from within the REPL. Set HF_TOKEN in your environment to authenticate (required for gated models; increases rate limits for all requests).

bash
# Search for GGUF repos by keyword (sorted by downloads)
/model hff search qwen3

# List GGUF files and sizes inside a repo
/model hff info unsloth/Qwen2.5-VL-7B-Instruct-GGUF

# Download a specific file - registers it as an alias in ~/.kdeps/gguf_versions.yaml
/model hff download unsloth/Qwen2.5-VL-7B-Instruct-GGUF Qwen2.5-VL-7B-Instruct-Q4_K_M.gguf

# Switch to it immediately after download
/model Qwen2.5-VL-7B-Instruct-Q4_K_M

/model hff download <repo> without a filename shows the available files (same as /model hff info). Downloaded files go to ~/.kdeps/models/ and the alias is the filename without the .gguf extension.

Managing running servers ​

/model ps shows all llamafile and llama-server processes started in the current session:

PID      PORT   BACKEND      MODEL                                STATUS
12345    8080   gguf         Qwen2.5-VL-7B-Instruct-Q4_K_M       healthy
12346    8081   file         phi4                                  loading
/model ps kill phi4           # send SIGKILL, remove port file
/model ps switch phi4         # set active model to an already-running server

See also ​

Released under the Apache 2.0 License.

Copyright (c) 2024-present kdeps contributors