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Model: Jenil05/Aether-1.5B-Agentic-core Source: Original Platform
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README.md
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README.md
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license: apache-2.0
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base_model: unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit
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tags:
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- agentic
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- function-calling
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- tool-use
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- tool-router
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- low-latency
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- unsloth
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- tensor-type:fp16
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metrics:
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- accuracy
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pipeline_tag: text-generation
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---
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# Aether-1.5B-Agentic-core
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<p align="left">
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<img src="https://img.shields.io/badge/IDENTITY-AETHER--1.5B--AGENTIC--CORE-cyan?style=for-the-badge&logo=cpu" alt="Identity: AETHER-1.5B-AGENTIC-CORE">
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</p>
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**Aether-1.5B-Agentic-core** is an elite, edge-native language model explicitly optimized to serve as the **core routing hub and tool-execution engine** for autonomous multi-agent orchestration frameworks (such as CrewAI, LangChain, and AutoGen).
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By optimizing the attention heads specifically for API schemas, this model bridges a massive architectural gap: enabling localized, low-latency deployment without sacrificing structural integrity during code output steps.
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---
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## ⚡ Technical Specifications
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| Feature | System Configuration |
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| :--- | :--- |
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| **Model Blueprint** | Qwen-2.5 (Instruct variant backplane) |
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| **Parameter Volume** | 1.54 Billion |
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| **Context Window** | 4,096 Tokens |
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| **Quantization Format** | Un-quantized; natively merged back into **16-bit Float (fp16)** |
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| **Inference VRAM Profile** | ~3.5 GB (Highly accessible for consumer hardware) |
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| **Primary Specialty** | Deterministic JSON-Schema Parsing & Argument Extraction |
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---
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## 🛠️ The Fine-Tuning Pipeline
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Standard small language models (<3B parameters) are notoriously fragile when handling code parameters—they frequently introduce syntax hallucinations, drop trailing brackets, or introduce verbose conversational fluff (*"Sure, let me call that function for you!"*) that instantly breaks automated software parsers.
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### 🧠 Training Strategy & Hyperparameters
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**Aether-1.5B-Agentic-core** was constructed via Parameter-Efficient Fine-Tuning (PEFT) using **Unsloth**:
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* **Quantization Method:** 4-bit QLoRA target module injection to maximize gradient headroom.
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* **Target Modules:** Complete attention mechanism coverage (`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`).
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* **Dataset Alignment:** Conditioned exclusively on high-fidelity multi-turn function invocation layouts from `NousResearch/hermes-function-calling-v1`.
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* **Baking Protocol:** Merged permanently back into structural base layers (`merged_16bit`), eliminating adapter latency overhead entirely.
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---
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## ⚙️ System Infrastructure & Stack Badges
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<p align="left">
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<img src="https://img.shields.io/badge/Optimization-Unsloth-green?style=flat-square&logo=python&logoColor=white" alt="Unsloth Optimized">
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<img src="https://img.shields.io/badge/Framework-PyTorch-EE4C2C?style=flat-square&logo=pytorch&logoColor=white" alt="PyTorch Framework">
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<img src="https://img.shields.io/badge/Dataset-Hermes%20Function-blue?style=flat-square&logo=databricks&logoColor=white" alt="Hermes Dataset">
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<img src="https://img.shields.io/badge/License-Apache%202.0-red?style=flat-square&logo=apache" alt="Apache 2.0 License">
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<img src="https://img.shields.io/badge/Compute-T4%20GPU-orange?style=flat-square&logo=nvidia&logoColor=white" alt="Compute Footprint">
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</p>
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---
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## 🚀 Behavioral Traits & Core Abilities
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1. **Deterministic Structured Layouts:** Hardened against schema syntax decay, guaranteeing valid, clean, parseable JSON payload extractions.
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2. **Zero-Dialogue Overhead:** Stripped of non-operational text arrays. The model targets raw arguments instantly, cutting down execution latency and compute token costs.
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3. **Strict Data-Type Preservation:** Natively correlates natural text variables into explicit system-level parameters (e.g., matching raw strings directly to accurate `int`, `boolean`, or `array` properties).
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---
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## 💻 Implementation & Inference Sample
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To trigger the tool-calling pathway natively, structure your system prompt with clear tool boundaries.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "Jenil05/Aether-1.5B-Agentic-core"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
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messages =
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