🇮🇳 Prakrit1.0-7B-Small
🌟 Made in India 🌟
📄 License: Apache 2.0 | ⚙️ Parameters: 7 Billion | 💻 Focus: Coding & Agents
📌 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:
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!