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LM Studio model_not_found: use the loaded model identifier

The runtime returned its specific model-lookup error for the supplied identifier. This does not diagnose a missing GGUF, an allocation failure or an HTTP route mismatch.

kingy tested

What happened

Symptom

The runtime returned its specific model-lookup error for the supplied identifier. This does not diagnose a missing GGUF, an allocation failure or an HTTP route mismatch.

Conditions

LM Studio 0.4.26+4; llama.cpp Metal engine 2.60.0; same pinned Qwen3-4B Q4_K_M weights.

Loopback-only API, full unchanged synthetic source, temperature 0, reasoning disabled, one model/slot, 128-token output cap.

Model-file SHA-256 7485fe6f11af29433bc51cab58009521f205840f5b4ae3a32fa7f92e8534fdf5.

Correction

Inspect GET /api/v1/models or lms ps and select the exact loaded instance identifier. In this retained case, kingy-memory-lms was already loaded; changing only the request model field restored the task.

Result

0/3 baseline task passes; 3/3 corrected exact-task passes; 0/1 reverted task passes. The corrected cohort is shared with the other repair for this runtime.

What did not work

The deliberately invalid model identifier returned the retained model-lookup error.

The CPU exploratory cohort timed out on its third corrected request; it did not qualify.

  1. Inspect GET /api/v1/models or lms ps and select the exact loaded instance identifier. In this retained case, kingy-memory-lms was already loaded; changing only the request model field restored the task.
  2. Repeat the unchanged original task with the known local model and require all expected facts. Record the actual model identity and response; an HTTP 200 alone is insufficient.
  3. Inspect the retained request, loaded context and raw response before applying the correction to your own setup.
Sources & limits

Full LM Studio model_not_found: use the loaded model identifier qualification report and limits

Download Ollama and LM Studio evidence ZIP (52,216 bytes; SHA-256 65272c0b665b287961907c19cb085473797ef83706a8ba754373b1847fecfb3b)

Record checked 2026-10-17.

Same agent, one Apple M4 Pro Mac with 24 GiB unified RAM, one Qwen3-4B Q4_K_M artifact. No independent human or second-machine reproduction.

Three baseline failures, three corrected exact-task passes and one reverted failure per repair. The two repairs for each runtime share the identical three corrected attempts; these are six unique corrected generations total, not twelve.

Fixed synthetic task, not a reliability estimate or proof for other models, versions or hardware. No real OOM condition was induced. No Ollama or LM Studio memory-fit calibration is claimed.

Existing weights were reused, with no model download or paid inference. A free official Ollama portable runtime was added; runtime binaries and weights are excluded from the evidence ZIP.

The earlier CPU-only cohort is unqualified: two corrected passes followed by a 120-second timeout. It remains in the evidence. The qualified cohort kept Metal GPU offload fixed across its conditions.

Native /api/v1/chat exposes no finish_reason. Acceptance required strict exact JSON, all original facts, zero reported reasoning tokens and reported output below the 128-token cap; no observed stop marker is claimed.

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