Update README.md

README.md

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 license: cc-by-nc-nd-4.0
 base_model:
 - Qwen/Qwen2.5-1.5B
----
+language:
+- en
+- de
+tags:
+- Function_Call
+- Automotive
+- SLM
+- GGUF
+---
+
+# Model Card: Qwen2.5-1.5B-Auto-FunctionCaller
+
+## Model Details
+
+* **Model Name:** Qwen2.5-1.5B-Auto-FunctionCaller
+* **Base Model:** [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B)
+* **Model Type:** Language Model fine-tuned for Function Calling.
+* **Recommended Quantization:** `Qwen2.5-1.5B-Auto-FunctionCaller.Q4_K_M_I.gguf`
+    * This GGUF file using Q4\_K\_M quantization with Importance Matrix is recommended as offering the best balance between performance and computational efficiency (inference speed, memory usage) based on evaluation.
+
+## Intended Use
+
+* **Primary Use:** Function calling extraction from natural language queries within an automotive context. The model is designed to identify user intent and extract relevant parameters (arguments/slots) for triggering vehicle functions or infotainment actions.
+* **Research Context:** This model was specifically developed and fine-tuned as part of a research publication investigating the feasibility and performance of Small Language Models (SLMs) for function-calling tasks in resource-constrained automotive environments.
+* **Target Environment:** Embedded systems or edge devices within vehicles where computational resources may be limited.
+* **Out-of-Scope Uses:** General conversational AI, creative writing, tasks outside automotive function calling, safety-critical vehicle control.
+
+## Performance Metrics
+
+The following metrics were evaluated on the `Qwen2.5-1.5B-Auto-FunctionCaller.Q4_K_M_I.gguf` model:
+
+* **Evaluation Setup:**
+    * Total Evaluation Samples: 2074
+* **Performance:**
+    * **Exact Match Accuracy:** 0.8414
+    * **Average Component Accuracy:** 0.9352
+* **Efficiency & Confidence:**
+    * **Throughput:** 10.31 tokens/second
+    * **Latency (Per Token):** 0.097 seconds
+    * **Latency (Per Instruction):** 0.427 seconds
+    * **Average Model Confidence:** 0.9005
+    * **Calibration Error:** 0.0854
+
+*Note: Latency and throughput figures are hardware-dependent and should be benchmarked on the target deployment environment.*
+
+## Limitations
+
+* **Domain Specificity:** Performance is optimized for automotive function calling. Generalization to other domains or complex, non-structured conversations may be limited.
+* **Quantization Impact:** The `Q4_K_M_I` quantization significantly improves efficiency but may result in a slight reduction in accuracy compared to higher-precision versions (e.g., FP16).
+* **Complex Queries:** May struggle with highly nested, ambiguous, or unusually phrased requests not well-represented in the fine-tuning data.
+* **Safety Criticality:** This model is **not** intended or validated for safety-critical vehicle operations (e.g., braking, steering). Use should be restricted to non-critical systems like infotainment and comfort controls.
+* **Bias:** Like any model, performance and fairness depend on the underlying data. Biases present in the fine-tuning or evaluation datasets may be reflected in the model's behavior.
+
+## Training Data (Summary)
+
+The model was fine-tuned on a synthetic dataset specifically curated for automotive function calling tasks. Details will be referenced in the associated publication.
+
+## Citation
+
+TBD