doc(README): finalize model card with clarified scope, evaluation details, and usage framing

README.md

@@ -28,7 +28,7 @@ This adapter is designed as a **demonstration and testbed** for structured recur
 
 ## 🚀 Uses
 
-This adapter enhances Phi-4-mini-instruct for parsing natural recurrence expressions into DSL format.
+This adapter fine-tunes Phi-4-mini-instruct for parsing natural recurrence expressions into DSL format.
 
 Example prompt:
 
@@ -42,32 +42,16 @@ It is a useful starting point for prototyping DSL-based schedulers, assistants,
 
 The DSL produced by this model is defined and executed by the [Mini Recurrence Converter DSL module](https://github.com/gh9869827/fifo-dev-dsl/tree/main/fifo_dev_dsl/domain_specific/mini_recurrence_converter_dsl).
 
-It provides:
-
-- 🧠 A symbolic DSL to represent natural language recurrence patterns  
-- 📅 A Python engine to evaluate the DSL into structured recurrence rules  
-- 🤖 An integration with LLMs to translate text into DSL code  
-- 🔒 A safe alternative to arbitrary code execution — only predefined DSL functions are supported, so untrusted model output **cannot execute arbitrary code**
-
-Example:
-
-```python
-from fifo_dev_dsl.domain_specific.mini_recurrence_converter_dsl import MiniRecurrenceConverterDSL
-dsl = "WEEKLY(1, [MO, WE], TIME(10, 0))"
-parsed = MiniRecurrenceConverterDSL().parse(dsl)
-print(parsed)
-# Output: structured recurrence rule object
-```
 
 ## ⚠️ Out-of-Scope Use
 
-This model is specialized for recurrence conversion DSL. It is not intended for general-purpose dialogue or unrelated tasks.
+This model has been fine-tuned specifically for recurrence conversion DSL as a demonstration and testbed. It is not intended for general-purpose dialogue or unrelated tasks.
 
 ## 🏗️ Training Details
 
-Trained on [a6188466/mini-recurrence-converter-dsl-dataset](https://huggingface.co/datasets/a6188466/mini-recurrence-converter-dsl-dataset) using the `dsl` adapter from [`fifo-tool-datasets`](https://github.com/gh9869827/fifo-tool-datasets) and [`fine_tune.py`](https://github.com/gh9869827/fifo-tool-airlock-model-env/blob/main/fifo_tool_airlock_model_env/fine_tuning/phi_4/fine_tune.py) in a fully local, air-gapped Docker container.
+Trained on [a6188466/mini-recurrence-converter-dsl-dataset](https://huggingface.co/datasets/a6188466/mini-recurrence-converter-dsl-dataset) using the `dsl` adapter from [`fifo-tool-datasets`](https://github.com/gh9869827/fifo-tool-datasets) and [`fine_tune.py`](https://github.com/gh9869827/fifo-tool-airlock-model-env/blob/main/fifo_tool_airlock_model_env/fine_tuning/phi_4/fine_tune.py).
 
-- **Dataset:** 226 examples mapping natural language to DSL commands, including hand-curated and [synthetic](https://github.com/gh9869827/fifo-dev-dsl/blob/main/fifo_dev_dsl/domain_specific/mini_recurrence_converter_dsl/generate_synthetic_data.py) samples
+- **Dataset:** 279 examples mapping natural language to DSL commands, including hand-curated and [synthetic](https://github.com/gh9869827/fifo-dev-dsl/blob/main/fifo_dev_dsl/domain_specific/mini_recurrence_converter_dsl/generate_synthetic_data.py) samples
 - **Epochs:** 15  
 - **Batch size:** 1  
 - **Precision:** bf16  
@@ -106,7 +90,7 @@ Trained on [a6188466/mini-recurrence-converter-dsl-dataset](https://huggingface.
 - **Eval set:** Natural language queries similar in structure and intent to the training examples  
 - **Metric:** Functional equivalence — two DSL expressions are considered correct when they evaluate to the same result  
 - **Results:**  
-  - **97.79%** on held-out [test set](https://huggingface.co/datasets/a6188466/mini-recurrence-converter-dsl-dataset) (**221/226** passed)  
+  - **97.79%** on a held-out [test set](https://huggingface.co/datasets/a6188466/mini-recurrence-converter-dsl-dataset) (**221/226** passed), containing queries similar in structure and intent to the training examples, using the functional equivalence metric defined above.
     The test set includes 26 hand-curated and 200 [synthetic](https://github.com/gh9869827/fifo-dev-dsl/blob/main/fifo_dev_dsl/domain_specific/mini_recurrence_converter_dsl/generate_synthetic_data.py) examples.
 
 These results demonstrate strong accuracy on tested queries, but performance should be carefully evaluated in each individual use case.