Instructions to use weareaspireai/AspireAI_v1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use weareaspireai/AspireAI_v1.2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="weareaspireai/AspireAI_v1.2")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("weareaspireai/AspireAI_v1.2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use weareaspireai/AspireAI_v1.2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "weareaspireai/AspireAI_v1.2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "weareaspireai/AspireAI_v1.2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/weareaspireai/AspireAI_v1.2
- SGLang
How to use weareaspireai/AspireAI_v1.2 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 "weareaspireai/AspireAI_v1.2" \ --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": "weareaspireai/AspireAI_v1.2", "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 "weareaspireai/AspireAI_v1.2" \ --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": "weareaspireai/AspireAI_v1.2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use weareaspireai/AspireAI_v1.2 with Docker Model Runner:
docker model run hf.co/weareaspireai/AspireAI_v1.2
Model Card for AspireAI 7B
AspireAI is specially trained to generate Multiple Choice Questions on 6 different subjects for UPSC Preliminary Exam.
Model Details
AspireAI addresses the imperative need for modernized and optimized learning experiences for UPSC aspirants preparing for the General Studie Paper – I of Preliminary exam. Recognizing the significance of MCQs, AspireAI can generate different MCQs upto the standards of UPSC questions for Aspirants to practice and learn.
Model Description
This model is a finetuned version of Llama 3.2 1B.
- Developed by: Vaishak Bhuvan M R, Kundan Rao S and Team
- Supported by: Dept. of AIML, B.M.S. College of Engineering, Bengaluru 560 019
- Finetuned from model : Llama 3.2 1B
Model Sources
- Paper : Coming Soon
- Demo : Coming Soon
Uses
- AspireAI can generate 5 MCQs at once on a single topic -
- The topics on which AspireAI can generate MCQs are:-
- History
- Indian Polity and Governance
- Indian Economy
- Science and General Technology
- Environment and Ecology
- Indian and World Geography
Risks, and Limitations
The model sometimes may generate wrong answers for the questions or might hallucinate to generate questions that are not related to the syllabus of the UPSC Exam
Training Details
Training Data
The Model has been trained on custom data collected by the team on Nvidia A5000 GPU, The Dataset cannot be disclosed for a few reasons.
Training Procedure
The model was trained using the Instruction Fine-tuning format specified by the Mistral AI Team.
Metrics
More info will be provided soon !!
Results
Coming soon !!
Project contact
For more info contact - mrvbhuvan@gmail.com