Instructions to use IEEEVITPune-AI-Team/ChatbotAlpha0.7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IEEEVITPune-AI-Team/ChatbotAlpha0.7 with Transformers:

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

pipe = pipeline("text-generation", model="IEEEVITPune-AI-Team/ChatbotAlpha0.7")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("IEEEVITPune-AI-Team/ChatbotAlpha0.7")
model = AutoModelForCausalLM.from_pretrained("IEEEVITPune-AI-Team/ChatbotAlpha0.7")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IEEEVITPune-AI-Team/ChatbotAlpha0.7 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IEEEVITPune-AI-Team/ChatbotAlpha0.7"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IEEEVITPune-AI-Team/ChatbotAlpha0.7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/IEEEVITPune-AI-Team/ChatbotAlpha0.7

SGLang

How to use IEEEVITPune-AI-Team/ChatbotAlpha0.7 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 "IEEEVITPune-AI-Team/ChatbotAlpha0.7" \
    --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": "IEEEVITPune-AI-Team/ChatbotAlpha0.7",
		"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 "IEEEVITPune-AI-Team/ChatbotAlpha0.7" \
        --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": "IEEEVITPune-AI-Team/ChatbotAlpha0.7",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use IEEEVITPune-AI-Team/ChatbotAlpha0.7 with Docker Model Runner:
```
docker model run hf.co/IEEEVITPune-AI-Team/ChatbotAlpha0.7
```

Model Card for Model ID

The motivation of this model was to educate the university students about the vast scope that technology has and channeling it via the chatbot we have created. This is just a small step that AI Team at IEEE SB VIT Pune has taken to contribute in the vast space of Artificial Intelligence. As our tagline says, "Advancing Technology for Humanity" we believe that AI can truly revolutionize the tech domain forever and hence we have invested in creating a chatbot.

Model Details

Model Description

This chatbot is curated by the AI Team(2023-24) at IEEE SB VIT Pune. The primary purpose of this bot is answer questions related to Data Structure and Algorithms, FAQs related to IEEE SB VIT Pune, Research paper based questions, Placement based questions and much more.

Developed by: AI Team (2023-24) [Mrunmayee Phadke (Project Head), Hritesh Maikap, Nidhish W, Arya Lokhande, Apurva Kota, Soham Nimale]
Funded by [optional]: IEEE SB VIT Pune
Shared by [optional]: [More Information Needed]
Model type: Text Generation based model trained on Llama2
Language(s): Python
Finetuned from model [optional]: Llama2
Repository: [Loading...]
Paper [optional]: [Loading...]

Uses

Direct Use

You can directly access the model from the space hosted on our repository.

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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Safetensors

Model size

7B params

Tensor type

F16

Dataset used to train IEEEVITPune-AI-Team/ChatbotAlpha0.7

Spaces using IEEEVITPune-AI-Team/ChatbotAlpha0.7 3

Paper for IEEEVITPune-AI-Team/ChatbotAlpha0.7

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 56