LM Studio Models

Offload tasks to local models when quality suffices. Base URL: http://127.0.0.1:1234. Auth: Authorization: Bearer lmstudio. instance_id = loaded_instances[].id (same model can have multiple, e.g. key and key:2).

Key Terms

• model: From GET models key; use in chat and optional load.
• lm_studio_api_url: Default http://127.0.0.1:1234 (paths /api/v1/...).
• response_id / previous_response_id: Chat returns response_id; pass as previous_response_id for stateful.
• instance_id: For unload, use only the value from GET /api/v1/models for that model: each loaded_instances[].id. Do not assume it equals the model key; with multiple instances ids can be like key:2. LM Studio docs: List (loaded_instances[].id), Unload (instance_id).

Trigger in frontmatter; below = implementation.

Prerequisites

LM Studio 0.4+, server :1234, models on disk; load/unload via API (JIT optional); Node for script (curl ok).

Quick start

Minimal path: list models, then one chat. Replace \x3Cmodel> with a key from GET /api/v1/models and \x3Ctask> with the task text.

curl -s -H 'Authorization: Bearer lmstudio' http://127.0.0.1:1234/api/v1/models
node scripts/lmstudio-api.mjs \x3Cmodel> '\x3Ctask>' --temperature=0.5 --max-output-tokens=200

Stateful multi-turn: pass --previous-response-id=\x3Cid> from the prior script output. Or use --stateful to persist response_id automatically. Optional --log \x3Cpath> for request/response.

node scripts/lmstudio-api.mjs \x3Cmodel> 'First turn...' --previous-response-id=$ID1
node scripts/lmstudio-api.mjs \x3Cmodel> 'Second turn...' --previous-response-id=$ID2

Complete Workflow

Step 0: Preflight

GET \x3Cbase>/api/v1/models; non-200 or connection error = server not ready.

exec command:"curl -s -o /dev/null -w '%{http_code}' -H 'Authorization: Bearer lmstudio' http://127.0.0.1:1234/api/v1/models"

Step 1: List Models and Select

GET /api/v1/models to list models. Parse each entry: key, type, loaded_instances, max_context_length, capabilities. If a model already has loaded_instances.length > 0 and fits the task, skip to Step 5; otherwise pick a key for chat (and optional load in Step 3). Choose by task: vision -> capabilities.vision; embedding -> type=embedding; context -> max_context_length. Prefer already-loaded; prefer smaller for speed, larger for reasoning. Note loaded_instances[].id for optional unload later.

Example — list models:

exec command:"curl -s -H 'Authorization: Bearer lmstudio' http://127.0.0.1:1234/api/v1/models"

Parse models[] (key, type, loaded_instances, max_context_length, capabilities, params_string). If a model has loaded_instances.length > 0 and fits task, skip to Step 5; else pick key for chat (and optional load). Note loaded_instances[].id for optional unload.

Step 2: Model Selection

Pick key from GET response; use as model in chat (optional load). Constraints: vision -> capabilities.vision; embedding -> type=embedding; context -> max_context_length. Prefer loaded (loaded_instances non-empty), smaller for speed/larger for reasoning; fallback primary. If unsure, use the first loaded instance for that key or the smallest loaded model that fits the task. Optional POST load; else JIT on first chat.

Step 3: Load Model (optional)

Optional: POST /api/v1/models/load { model, context_length?, ... }. Or run scripts/load.mjs <model>. JIT: first chat loads; explicit load only for specific options.

Step 4: Verify Loaded (optional)

If explicit load: GET models, confirm loaded_instances. If JIT: no verify; first chat returns model_instance_id, stats.model_load_time_seconds.

Step 5: Call API

From the skill folder: node scripts/lmstudio-api.mjs <model> '<task>' [options].

exec command:"node scripts/lmstudio-api.mjs \x3Cmodel> '\x3Ctask>' --temperature=0.7 --max-output-tokens=2000"

Stateful: add --previous-response-id=\x3Cresponse_id>. Curl: POST \x3Cbase>/api/v1/chat, body model, input, store, temperature, max_output_tokens; optional previous_response_id. Parse: output (type message) -> content; response_id, model_instance_id, stats. Script outputs content, model_instance_id, response_id, usage.

Step 6: Unload (optional)

For the model key you used: GET /api/v1/models, then for each loaded_instances[].id for that model, POST /api/v1/models/unload with body {"instance_id": "\x3Cthat id>"}. Use the id from the response only (do not send the model key unless it exactly equals that id). Or run scripts/unload.mjs <model_key> (script does GET then unloads each instance id). Optional --unload-after (default off); use --keep to leave loaded. Unload only that model's instances. JIT+TTL auto-unload; explicit when needed.

# One unload per instance_id; repeat for each id in that model's loaded_instances
exec command:"curl -s -X POST http://127.0.0.1:1234/api/v1/models/unload -H 'Content-Type: application/json' -H 'Authorization: Bearer lmstudio' -d '{\"instance_id\": \"\x3Cinstance_id>\"}'"

Step 7: Verify unload (optional)

After unloading, confirm no instances remain for that model key. Run the jq check below; result must be 0. If non-zero, unload the remaining instance_id(s) from that model and re-run the check. Do not infer from "model object exists"; the object still exists with an empty loaded_instances array.

exec command:"curl -s -H 'Authorization: Bearer lmstudio' http://127.0.0.1:1234/api/v1/models | jq '.models[]|select(.key==\"\x3Cmodel_key>\")|.loaded_instances|length'"

Expect output 0. If not, unload remaining instance_ids and re-run.

Error Handling

• Script retries on transient failure (2-3 attempts with backoff).
• Model not found -> pick another model from GET response.
• API/server errors -> GET models, check URL.
• Invalid output -> retry.
• Memory -> unload or smaller model.
• Unload fails -> instance_id must be exactly from GET /api/v1/models for that model's loaded_instances[].id (not the model key unless it matches).

Copy-paste

Replace \x3Cmodel> with a key from GET /api/v1/models and \x3Ctask> with the task text. Optional unload per Step 6 (instance_id from GET models for that key).

exec command:"curl -s -H 'Authorization: Bearer lmstudio' http://127.0.0.1:1234/api/v1/models"
exec command:"node scripts/lmstudio-api.mjs \x3Cmodel> '\x3Ctask>' --temperature=0.7 --max-output-tokens=2000"

LM Studio API Details

Helper/API: see Step 5. Output: content, model_instance_id, response_id, usage. Auth: Bearer lmstudio. List GET /api/v1/models. Load POST /api/v1/models/load (optional). Unload POST /api/v1/models/unload { instance_id }.

Scripts

lmstudio-api.mjs: chat; options --stateful, --unload-after, --keep, --log <path>, --previous-response-id, --temperature, --max-output-tokens. load.mjs: load model by key. unload.mjs: unload by model key (all instances). test.mjs: smoke test (load, chat, unload one model).

Notes

• LM Studio 0.4+.
• JIT (first chat loads; model_load_time_seconds in stats); stateful (response_id / previous_response_id).