Instructions to use Anoopsingh53/NextBharat-V2-Final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Anoopsingh53/NextBharat-V2-Final with Transformers:

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

pipe = pipeline("text-generation", model="Anoopsingh53/NextBharat-V2-Final")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Anoopsingh53/NextBharat-V2-Final")
model = AutoModelForCausalLM.from_pretrained("Anoopsingh53/NextBharat-V2-Final", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Anoopsingh53/NextBharat-V2-Final with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Anoopsingh53/NextBharat-V2-Final"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Anoopsingh53/NextBharat-V2-Final",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Anoopsingh53/NextBharat-V2-Final

SGLang

How to use Anoopsingh53/NextBharat-V2-Final 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 "Anoopsingh53/NextBharat-V2-Final" \
    --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": "Anoopsingh53/NextBharat-V2-Final",
		"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 "Anoopsingh53/NextBharat-V2-Final" \
        --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": "Anoopsingh53/NextBharat-V2-Final",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use Anoopsingh53/NextBharat-V2-Final with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Anoopsingh53/NextBharat-V2-Final to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Anoopsingh53/NextBharat-V2-Final to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Anoopsingh53/NextBharat-V2-Final to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Anoopsingh53/NextBharat-V2-Final",
    max_seq_length=2048,
)

Docker Model Runner
How to use Anoopsingh53/NextBharat-V2-Final with Docker Model Runner:
```
docker model run hf.co/Anoopsingh53/NextBharat-V2-Final
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🚀 NextBharat V2 Final

NextBharat V2 is a fine-tuned version of Llama-3.1-8B, specifically optimized for regional Indian languages and student-centric queries. Developed at NextMatrix Lab, this model aims to bridge the language gap for students in Tier 2 and Tier 3 cities.

✨ Key Features:

Regional Support: Trained to understand and respond in multiple Indian regional contexts.
Student Focused: Optimized for competitive exam guidance (SSC, Railway, etc.) and technical subjects like Compiler Design and DBMS.
Lightweight & Fast: Finetuned using Unsloth for 4-bit quantization, making it efficient for inference.

🛠️ Technical Details:

Base Model: unsloth/llama-3.1-8b-bnb-4bit
Training Method: LoRA (Rank 16)
Developer: Anoop Singh (Founder, NextMatrix Lab)

📖 How to Use:

from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained("Anoopsingh53/NextBharat-V2-Final")
# Uploaded finetuned  model

- **Developed by:** Anoopsingh53
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.1-8b-bnb-4bit

This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Anoopsingh53/NextBharat-V2-Final

Quantizations

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