Instructions to use Harsh2026verma/code-generator-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Harsh2026verma/code-generator-model with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Harsh2026verma/code-generator-model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Harsh2026verma/code-generator-model")
model = AutoModelForCausalLM.from_pretrained("Harsh2026verma/code-generator-model")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Harsh2026verma/code-generator-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Harsh2026verma/code-generator-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Harsh2026verma/code-generator-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Harsh2026verma/code-generator-model

SGLang

How to use Harsh2026verma/code-generator-model with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Harsh2026verma/code-generator-model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Harsh2026verma/code-generator-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Harsh2026verma/code-generator-model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Harsh2026verma/code-generator-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Harsh2026verma/code-generator-model with Docker Model Runner:
```
docker model run hf.co/Harsh2026verma/code-generator-model
```

🔒 Hinglish Cybersecurity & Web Development Expert

Model Overview

HinglishCyberSec is a fine-tuned CodeLlama-7B model specialized in multilingual cybersecurity and full-stack web development. It understands and generates code in Hindi, English, and Hinglish (Hindi+English mix).

🎯 What This Model Does

Capability	Description
🌐 Multilingual	Responds in Hindi, English, or Hinglish
🛡️ Cybersecurity	SQL injection, XSS, DDoS, JWT, Encryption, CSRF protection
💻 Web Development	HTML, CSS, JavaScript, React, Flask, Express, PHP
🔒 Security Headers	CSP, HSTS, X-Frame-Options implementation
📊 Rate Limiting	DDoS protection, API rate limiting
🔐 Authentication	JWT, bcrypt, session management
📝 Code Explanation	Explains code in simple Hinglish

Model Details

Basic Information

Developed by: Harsh Verma
Model type: Causal Language Model (Fine-tuned CodeLlama-7B)
Language(s): Hindi, English, Hinglish (Hindi + English mix)
License: MIT
Base Model: CodeLlama-7B
Fine-tuned on: Custom multilingual cybersecurity + web development dataset

Model Sources

Repository: [Your ]
Demo: [https://huggingface.co/spaces/Harsh2026verma/Rudra-chatbot]
Dataset: [Link to your dataset]

Uses

Direct Use

This model can be used for:

# 1. Generate secure code in Hinglish
response = model.generate("SQL injection se bachne ka tarika batao")

# 2. Create web applications
response = model.generate("Ek responsive navbar banao with logo and 3 links")

# 3. Security audit
response = model.generate("Is code mein SQL injection vulnerability hai?")

# 4. DDoS protection
response = model.generate("Flask mein rate limiting kaise lagayein?")

Downloads last month: 90

Safetensors

Model size

0.2B params

Tensor type

F32

Harsh2026verma
/

code-generator-model