---
license: apache-2.0
tags:
- text-generation-inference
- transformers
- code
- agent
- text-generation
- custom-tune
base_model:
- Qwen/Qwen2.5-7B
language:
- en
pipeline_tag: text-generation
---

<div align="center">

# 🇮🇳 Prakrit1.0-7B-Small

## 🌟 **Made in India** 🌟

<h3>🚀 A High-Performance Specialist Model</h3>

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

---

`📄 License: Apache 2.0` | `⚙️ Parameters: 7 Billion` | `💻 Focus: Coding & Agents`

</div>

---

## 📌 Overview

**Prakrit1.0-7B-Small** is an advanced 7-billion parameter large language model proudly developed by **Jagneshdeveloper**. Built on top of the powerful **Qwen2.5** architecture, this model has been custom-engineered and fine-tuned specifically for autonomous agentic workflows and elite coding tasks. 

By optimizing the baseline capabilities, Prakrit1.0-7B-Small delivers rapid, highly accurate code synthesis and structured logical reasoning.

---

## ⚡ Key Capabilities

* **💻 Coding Specialist:** Optimized to write, debug, explain, and refactor complex code across multiple programming languages (Python, JavaScript, C++, Go, etc.).
* **🤖 Agentic Excellence:** Engineered with a strong grasp of tool-use planning, step-by-step reasoning, and generating strictly formatted outputs (like JSON or system commands).
* **🌐 Multitask Efficiency:** Maintains top-tier performance in standard text generation, summarisation, and translation tasks.

---

## 📊 Model Summary

* **Model Name:** Prakrit1.0-7B-Small
* **Developer:** Jagneshdeveloper
* **Base Architecture:** Built on top of Qwen2.5
* **Parameters:** 7 Billion (7B)
* **License:** Apache 2.0
* **Primary Language:** English (en)

---

## 💻 Quick Start

You can quickly load and deploy **Prakrit1.0-7B-Small** using the Hugging Face `transformers` library:

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

model_name = "Jagneshdeveloper/prakrit1.0-7b-small"

# Load the optimized tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    device_map="auto", 
    torch_dtype=torch.bfloat16
)

# Test prompt for coding/agentic workflow
prompt = "Write a Python script to scrape website data and format it into a structured JSON array."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.3)

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

---

## 🛠️ Intended Uses & Limitations

### Ideal Use Cases
* Building autonomous AI agents and execution loops.
* Serving as an on-device or cloud-hosted programming assistant.
* Handling complex data extraction and formatting.

### Limitations
* As a 7B model, users should verify complex logic or math outputs before deploying code directly into production environments.
* Performance on highly specialized regional tasks may vary based on your prompt structures.

---

## 🤝 Attribution & Support

Created with ❤️ by **Jagneshdeveloper** in India. This model is distributed under the Apache 2.0 license. For feedback, feature requests, or collaborations, feel free to open a discussion in the community tab!