dvcrn 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 <model> with a key from GET /api/v1/models and <task> with the task text.
curl -s -H 'Authorization: Bearer lmstudio' http://127.0.0.1:1234/api/v1/models
node scripts/lmstudio-api.mjs <model> '<task>' --temperature=0.5 --max-output-tokens=200
Stateful multi-turn: pass --previous-response-id=<id> from the prior script output. Or use --stateful to persist response_id automatically. Optional --log <path> for request/response.
node scripts/lmstudio-api.mjs <model> 'First turn...' --previous-response-id=$ID1
node scripts/lmstudio-api.mjs <model> 'Second turn...' --previous-response-id=$ID2
Complete Workflow
Step 0: Preflight
GET /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 <model> '<task>' --temperature=0.7 --max-output-tokens=2000"
Stateful: add --previous-response-id=. Curl: POST /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": "<that 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\": \"<instance_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==\"<model_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 <model> with a key from GET /api/v1/models and <task> 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 <model> '<task>' --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).