Update README.md

README.md

@@ -3,7 +3,7 @@ license: apache-2.0
 language:
   - en
   - zh
-base_model: TokenRhythm/NeoHorse-Jev-4B
+base_model: TokenRhythm/NeoHorse-1-4B
 base_model_relation: finetune
 library_name: pytorch
 pipeline_tag: text-classification
@@ -18,16 +18,15 @@ tags:
 
 # Decider-4B
 
-**Decider-4B** is a structured decision model fine-tuned from **TokenRhythm/NeoHorse-Jev-4B** using LoRA on our collected decision datasets, followed by global temperature calibration. It returns typed probability distributions (`noul`, `choice`, `score`) using the NeoHorse pointer head. It is not a chat-generation model.
+Decider-4B is a structured decision model based on TokenRhythm/NeoHorse-1-4B. It uses LoRA to fine-tune the base model and trains the decision head from scratch on our collected decision datasets. The model then undergoes global temperature calibration to further calibrate its outputs. It produces typed probability distributions (noul, choice, score) and is not a chat-generation model.
 
 ## Training and calibration
 
-The project data collection includes typed-decisions train, direct classification tasks, TRAM, HelpSteer2, and constructed decision examples. The project recipes hold out validation data and reserve the benchmark test split for final evaluation. Exact run settings, where recorded by the exporter, are retained in `model_manifest.json`; reference scripts under `provenance/recipe_reference/` describe the project recipes rather than prove the exact mixture or hyperparameters of this checkpoint. No additional head-only training phase is claimed for this release.
+Exact run settings, where recorded by the exporter, are retained in model_manifest.json; reference scripts under provenance/recipe_reference/ describe the project recipes rather than prove the exact mixture or hyperparameters of this checkpoint. No additional head-only training phase is claimed for this release.
 
-The calibration implementation fits a single positive temperature on held-out validation soft labels by minimizing cross entropy. Probabilities are computed as `softmax(logits / temperature)`. The calibration stage does not modify pointer-head tensors; a positive temperature preserves the argmax class. The temperature and available calibration records are shipped with the model.
+The calibration implementation fits a single positive temperature on held-out validation soft labels by minimizing cross entropy. Probabilities are computed as softmax(logits / temperature). The calibration stage does not modify pointer-head tensors; a positive temperature preserves the argmax class. The temperature and available calibration records are shipped with the model.
 
 This is a **general model fine-tuned on benchmark train**, not a zero-shot submission. Its scores belong in the fitted/fine-tuned category. The precise upstream base commit was not recorded and is not inferred here. Training GPU and exact hyperparameters are not claimed when absent from the exported metadata.
-
 ## Format
 
 The training exporter has already merged LoRA into the backbone. No second LoRA merge, tensor conversion, or quantization is required. The release preserves:
@@ -37,7 +36,7 @@ The training exporter has already merged LoRA into the backbone. No second LoRA
 - `pointer_head.safetensors`: independent decision pointer head.
 - `model_manifest.json`: architecture and calibrated temperature.
 - `calibration.json`, when present: calibration evidence.
-- `vllm-fast/`: the vLLM adapter and its own decision runtime, including temperature support.
+- `vllm-fast-text/`: the vLLM adapter and its own decision runtime, including temperature support.
 
 Use the bundled runtime to load the complete directory. Loading only the backbone with a generic chat pipeline omits the decision head and temperature calibration.