Instructions to use Anoopsingh53/NextBharat-V2-Final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| base_model: unsloth/llama-3.1-8b-bnb-4bit | |
| tags: | |
| - text-generation-inference | |
| - transformers | |
| - unsloth | |
| - llama | |
| license: apache-2.0 | |
| language: | |
| - en | |
| # ๐ 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. | |
| ### โจ 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. | |
| ### ๐ ๏ธ Technical Details: | |
| - **Base Model:** unsloth/llama-3.1-8b-bnb-4bit | |
| - **Training Method:** LoRA (Rank 16) | |
| - **Developer:** Anoop Singh (Founder, NextMatrix Lab) | |
| ### ๐ How to Use: | |
| ```python | |
| 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) | |