Prakrit1.0-7b-small

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🇮🇳 Prakrit1.0-7B-Small

🌟 Made in India 🌟

<h3>🚀 A High-Performance Specialist Model</h3> <p align="center"> Developed by <b>Jagneshdeveloper</b> </p>

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

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📌 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.

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⚡ 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.

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📊 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)

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💻 Quick Start

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

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))

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🛠️ 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.

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🤝 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!