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
```

Update README.md

by Over9012 - opened Feb 19, 2024

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@@ -7,7 +7,7 @@ language:
 ---
 # Model Card for Model ID
-The motivation of them 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 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