Remove Eider serving internals from model card

README.md

@@ -39,7 +39,7 @@ Supply context, a question, and the allowed answers. The model scores those answ
 Use it for workflow routing, tool-call validation, evidence checks, rubric scoring, and action selection.
 Questions and answer meanings are supplied at request time, rather than fixed to a single classification task.
 
-The model was fine-tuned with PyTorch and evaluated using [Eider](https://github.com/rdaum/eider), a Rust/CUDA inference server optimized for DGX Spark.
+The model was fine-tuned with PyTorch and evaluated using [Eider](https://github.com/rdaum/eider), optimized for DGX Spark.
 The GGUF also works with llama.cpp's native decision endpoint. Use the [Dory support PR](https://github.com/ggml-org/llama.cpp/pull/30306) branch or an upstream version that includes it once merged.
 
 ## Model and formats
@@ -92,13 +92,6 @@ The runtime enforces the answer type and option membership. The model supplies t
 This method supports the answer types above, rather than arbitrary JSON schemas.
 The exact prompt and label mapping matter; the GGUF includes the decision template.
 
-### Eider serving
-
-Eider exposes `/v1/decisions` for a shared context and multiple typed questions.
-It processes the context once, then evaluates independent question branches in batches.
-Questions cannot attend to each other. Eider reuses cached context and reads only the vocabulary rows for answer labels.
-These optimizations reduce repeated work without requiring an Eider-specific classification head in the weights.
-
 ## Training data
 
 The following sources provide the decision questions and targets used for fine-tuning.
@@ -145,7 +138,7 @@ Within each comparison, base Dory and the fine-tune use the same quantization an
 The 512-question test set was scored after checkpoint selection on a separate 1,024-question validation set.
 
 Base Dory is the original NVIDIA BF16 model quantized with the same NVFP4 recipe used for this fine-tune.
-Both models use the same questions, typed-decision prompt, answer labels, two loops, and Eider executable.
+Both models use the same questions, typed-decision prompt, answer labels, two loops, and inference settings.
 This comparison measures the effect of fine-tuning within this decision interface, rather than Dory’s performance with other prompting methods.
 [Comparison results and hashes](evaluation/base-comparison.json) record the matched setup.
 
@@ -159,7 +152,7 @@ This comparison measures the effect of fine-tuning within this decision interfac
 ### llama.cpp — Q8_0
 
 The native `/v1/systemone` endpoint scored both models with Q8_0 weights.
-All 512 prompt token sequences and answer-label mappings matched Eider for each model.
+All 512 questions use identical prompt tokens and answer labels across both runtimes.
 This measures fine-tuning under llama.cpp; differences from the NVFP4 table can reflect both quantization and runtime arithmetic.
 
 | Test task | Questions | Base Dory accuracy | Typed Decisions accuracy | Base Brier ↓ | Typed Decisions Brier ↓ |