| --- |
| license: apache-2.0 |
| base_model: unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit |
| tags: |
| - agentic |
| - function-calling |
| - tool-use |
| - tool-router |
| - low-latency |
| - unsloth |
| - tensor-type:fp16 |
| metrics: |
| - accuracy |
| pipeline_tag: text-generation |
| --- |
| |
| # Aether-1.5B-Agentic-core |
|
|
| <p align="left"> |
| <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"> |
| </p> |
|
|
| **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). |
|
|
| 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. |
|
|
| --- |
|
|
| ## ⚡ Technical Specifications |
|
|
| | Feature | System Configuration | |
| | :--- | :--- | |
| | **Model Blueprint** | Qwen-2.5 (Instruct variant backplane) | |
| | **Parameter Volume** | 1.54 Billion | |
| | **Context Window** | 4,096 Tokens | |
| | **Quantization Format** | Un-quantized; natively merged back into **16-bit Float (fp16)** | |
| | **Inference VRAM Profile** | ~3.5 GB (Highly accessible for consumer hardware) | |
| | **Primary Specialty** | Deterministic JSON-Schema Parsing & Argument Extraction | |
|
|
| --- |
|
|
| ## 🛠️ The Fine-Tuning Pipeline |
|
|
| 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. |
|
|
| ### 🧠 Training Strategy & Hyperparameters |
| **Aether-1.5B-Agentic-core** was constructed via Parameter-Efficient Fine-Tuning (PEFT) using **Unsloth**: |
| * **Quantization Method:** 4-bit QLoRA target module injection to maximize gradient headroom. |
| * **Target Modules:** Complete attention mechanism coverage (`q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`). |
| * **Dataset Alignment:** Conditioned exclusively on high-fidelity multi-turn function invocation layouts from `NousResearch/hermes-function-calling-v1`. |
| * **Baking Protocol:** Merged permanently back into structural base layers (`merged_16bit`), eliminating adapter latency overhead entirely. |
|
|
| --- |
|
|
| ## ⚙️ System Infrastructure & Stack Badges |
|
|
| <p align="left"> |
| <img src="https://img.shields.io/badge/Optimization-Unsloth-green?style=flat-square&logo=python&logoColor=white" alt="Unsloth Optimized"> |
| <img src="https://img.shields.io/badge/Framework-PyTorch-EE4C2C?style=flat-square&logo=pytorch&logoColor=white" alt="PyTorch Framework"> |
| <img src="https://img.shields.io/badge/Dataset-Hermes%20Function-blue?style=flat-square&logo=databricks&logoColor=white" alt="Hermes Dataset"> |
| <img src="https://img.shields.io/badge/License-Apache%202.0-red?style=flat-square&logo=apache" alt="Apache 2.0 License"> |
| <img src="https://img.shields.io/badge/Compute-T4%20GPU-orange?style=flat-square&logo=nvidia&logoColor=white" alt="Compute Footprint"> |
| </p> |
|
|
| --- |
|
|
| ## 🚀 Behavioral Traits & Core Abilities |
|
|
| 1. **Deterministic Structured Layouts:** Hardened against schema syntax decay, guaranteeing valid, clean, parseable JSON payload extractions. |
| 2. **Zero-Dialogue Overhead:** Stripped of non-operational text arrays. The model targets raw arguments instantly, cutting down execution latency and compute token costs. |
| 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). |
|
|
| --- |
|
|
| ## 💻 Implementation & Inference Sample |
|
|
| To trigger the tool-calling pathway natively, structure your system prompt with clear tool boundaries. |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import torch |
| |
| model_id = "Jenil05/Aether-1.5B-Agentic-core" |
| tokenizer = AutoTokenizer.from_pretrained(model_id) |
| model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") |
| |
| messages = |
