beyoru
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EvolLLM-Linh

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 ---
 library_name: transformers
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 ---
 library_name: transformers
+tags:
+- tool
+- function-calling
+- agent
+base_model:
+- Qwen/Qwen3-4B-Instruct-2507
 ---
+# 🧠 **Model Card — EvolLLM-Linh**
+### **Model Overview**
+**Name:** EvolLLM-Linh
+**Version:** v1.0
+**Release Date:** October 23, 2025
+**Base Model:** [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
+**Library:** 🤗 *Transformers*
+**Purpose:**
+EvolLLM-Linh is a fine-tuned large language model designed for **function calling** and **multi-turn contextual reasoning**.
+It aims to benchmark and enhance the **robustness, accuracy, and dialogue coherence** of LLMs operating in **API-driven or tool-using environments**.
+**Key Capabilities:**
+* Precise and context-aware API invocation
+* Robust multi-turn dialogue consistency
+* Adaptive understanding of user preferences and intent shifts
+---
+### **Evaluation Summary**
+| **Category**                    | **Accuracy** |
+| ------------------------------- | -----------: |
+| SINGLE TURN – SINGLE FUNCTION   |        0.800 |
+| SINGLE TURN – PARALLEL FUNCTION |        0.660 |
+| MULTI TURN – USER ADJUST        |        0.500 |
+| MULTI TURN – USER SWITCH        |        0.620 |
+| SIMILAR API CALLS               |        0.760 |
+| USER PREFERENCE HANDLING        |        0.600 |
+| ATOMIC TASK – BOOLEAN           |        0.880 |
+| ATOMIC TASK – ENUM              |        0.940 |
+| ATOMIC TASK – NUMBER            |        0.940 |
+| ATOMIC TASK – LIST              |        0.920 |
+| ATOMIC TASK – OBJECT (DEEP)     |        0.580 |
+| ATOMIC TASK – OBJECT (SHORT)    |        0.800 |
+**Overall Accuracy:** **0.750 (75.0%)** in normal task (summary in leaderboard)
+---
+### **Leaderboard Reference**
+EvolLLM-Linh participates in evaluations aligned with **[ACEBench](https://chenchen0103.github.io/ACEBench/)** — a public leaderboard assessing LLM performance on **function calling, compositional reasoning, and multi-turn interaction**.
+Results here are preliminary and reflect internal benchmarking on the same task categories.
+---
+### **License**
+**MIT License** — free for research and non-commercial use with attribution.
+© 2025 beyoru.
+---