---
license: apache-2.0
tags:
- text-generation-inference
- transformers
- code
- agent
- text-generation
- custom-tune
- slerp-merge
- ties-merge
- reasoning
base_model:
- microsoft/phi-4
- microsoft/Phi-4-reasoning-plus
language:
- en
pipeline_tag: text-generation
---

<div align="center">

# 🇮🇳 Ekant-14B-small (Agentic Reasoning Edition)

## 🌟 **Made in India** 🌟

<h3>🚀 A High-Performance Specialist Model Fused with Deep Reasoning</h3>

<p align="center">
  Developed by <b>Jagneshdeveloper</b>
</p>

---

`📄 License: Apache 2.0` | `⚙️ Parameters: 14 Billion` | `💻 Focus: Elite Coding, Reasoning & Agents`

</div>

---

## 📌 Overview

**Ekant-14B-small** is an advanced 14-billion parameter large language model proudly developed by **Jagneshdeveloper**. While initially initialized via custom-trained adapter matrices, this final artifact is a **fully unquantized standalone model** in true `float16` precision. 

Built on top of the powerful **microsoft/phi-4** architecture, this model has been custom-engineered and cross-compiled across multiple advanced mathematical optimization passes (including **SLERP** and **TIES** multi-model fusion protocols) to integrate elite agentic logic with deep, multi-step validation tracking.

---

## 🔬 Fusing & Pipeline Lifecycle

This model was compiled under a strict resource-constrained hardware architecture using custom disk-free sharded watchdog pipelines to guarantee full float precision mapping without accuracy loss:
1. **LoRA Fine-Tuning**: Initial specialized instruction sets were targeted and compiled into low-rank matrix sub-layers (`ekant-adapter`).
2. **Base Integration**: Unquantized adapter weights were chemically baked directly back into the core 29.3GB `microsoft/phi-4` tensor layers.
3. **Vanilla Alignment**: Merged via **SLERP** (Spherical Linear Interpolation) at a calibrated `0.6/0.4` ratio back with the foundational base model to act as a stabilizing anchor and counteract catastrophic forgetting.
4. **Deep Reasoning Injection**: Fused via **TIES** (Trimming, Electing, and Merging) to combine our custom capabilities directly with reinforcement-learned o3-style logic pathways.

---

## ⚡ Key Capabilities

* **🧠 Deep Reasoning plus**: Features integrated reflection traces, error self-correction blocks, and highly granular problem-solving structures.
* **💻 Coding Specialist**: Optimized to write, debug, analyze, and safely refactor high-complexity code structures across Python, JavaScript, C++, Rust, and Go.
* **🤖 Agentic Excellence**: High structural accuracy for tool-use, functional api execution loops, and generating strictly formatted outputs (like complex JSON or nested system commands).

---

## 📊 Model Summary

* **Model Name:** Ekant-14B-small (Agentic Ultimate Edition)
* **Developer:** Jagneshdeveloper
* **Base Architecture:** Built on top of Microsoft Phi-4 (Phi3 For Causal LM Core Class)
* **Parameters:** 14 Billion (14B)
* **License:** Apache 2.0 (Permissive Open-Source)
* **Primary Language:** English (en)

---

## 💻 Quick Start

You can quickly load and deploy **Ekant-14B-small** using the Hugging Face `transformers` library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Real repository target path verified on your profile
model_name = "Jagneshdeveloper/ultimate-Ekant-14b"

# Load the optimized tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    device_map="auto", 
    torch_dtype=torch.float16,
    trust_remote_code=True
)

# Test prompt for deep reasoning & agentic execution
prompt = "Write an optimized Python script to scrape website data dynamically, handle API authentication token refreshes, and format it into a structured JSON array."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(
    **inputs, 
    max_new_tokens=512, 
    temperature=0.5, 
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)

print(tokenizer.decode(outputs, skip_special_tokens=True))
```

---

## 🛠️ Intended Uses & Limitations

### Ideal Use Cases
* Building autonomous AI agents and automated API execution loops.
* Serving as a local or cloud-hosted programming and mathematical reasoning assistant.
* Handling multi-step logical text generation and complex data extraction tasks.

### Limitations
* Standard 14B computing constraints apply; heavy inference calls may require sharding or quantization depending on available VRAM allocations.
* Users should verify complex logic outputs before running generated scripts straight into a live production workspace.

---

## 🤝 Attribution & Support

Created with ❤️ by **Jagneshdeveloper** in India. This model is distributed under the open and permissive **Apache 2.0 License**, providing full freedom for commercial deployment, modifications, and distributed derivatives. 

Special credit and attribution are extended to **Microsoft** for their foundational open-weights research contributions (`phi-4` and `Phi-4-reasoning-plus`), which served as the essential structural pillars and base anchors for this advanced mathematical crossover fusion project. 

For feedback, feature requests, or collaborations, feel free to open a discussion in the community tab!