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docs(server): drop the unimplemented lora_adapter request field #427

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docs(server): drop the unimplemented lora_adapter request field #427

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4 changes: 4 additions & 0 deletions docs/LORA.md
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Original file line number Diff line number Diff line change
@@ -1,5 +1,9 @@
# LoRA Adapter Support — dotLLM

> **Status**: design spec, not implemented. This is roadmap step 47. Only the `IAdapterManager`
> abstraction and `InferenceRequest.AdapterId` exist today — there is no adapter loader, no server
> endpoint, and no per-request adapter selection. Everything below describes the intended design.

## Overview

LoRA (Low-Rank Adaptation) enables fine-tuned model behaviors without modifying base weights. Multiple adapters can coexist on the same base model, with per-request adapter selection.
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11 changes: 9 additions & 2 deletions docs/SERVER.md
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Expand Up @@ -28,11 +28,18 @@ Primary chat endpoint. Accepts OpenAI-compatible request format.
"logit_bias": {"1234": -100},
"frequency_penalty": 0.5,
"presence_penalty": 0.3,
"n": 1,
"lora_adapter": "customer-support"
"n": 1
}
```

> **Adapter selection**: there is no `lora_adapter` request field (it is not part of the OpenAI
> surface either, and the server ignores unknown fields silently). LoRA serving is roadmap step 47
> and is not implemented: the engine has the `IAdapterManager` abstraction and
> `InferenceRequest.AdapterId`, but there is no implementation, no adapter admin endpoint, and no
> way to select an adapter per request. Until then, serve an adapted model by merging the adapter
> into the weights offline and loading the merged model — via `--model` at startup or
> `POST /v1/models/load`. See [LORA.md](LORA.md) for the planned design.

**Response** (non-streaming):
```json
{
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