Instructions to use SimuIation/CDM_Digital_Navigator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SimuIation/CDM_Digital_Navigator with Transformers:

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

pipe = pipeline("text-generation", model="SimuIation/CDM_Digital_Navigator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SimuIation/CDM_Digital_Navigator")
model = AutoModelForCausalLM.from_pretrained("SimuIation/CDM_Digital_Navigator")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SimuIation/CDM_Digital_Navigator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SimuIation/CDM_Digital_Navigator

SGLang

How to use SimuIation/CDM_Digital_Navigator 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 "SimuIation/CDM_Digital_Navigator" \
    --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": "SimuIation/CDM_Digital_Navigator",
		"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 "SimuIation/CDM_Digital_Navigator" \
        --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": "SimuIation/CDM_Digital_Navigator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SimuIation/CDM_Digital_Navigator with Docker Model Runner:
```
docker model run hf.co/SimuIation/CDM_Digital_Navigator
```

SimuIation commited on Jul 9, 2024

Commit

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@@ -19,7 +19,7 @@ This modelcard aims to be a base template for new models. It has been generated
 ### Model Description
-The Digital Navigator model is designed to assist users by generating natural language responses to input queries. It is a fine-tuned GPT-2 model, customized for specific use cases.
 - **Developed by:** Papagiannakis Panagiotis
 - **Model type:** GPT-2 (Generative Pre-trained Transformer 2)
@@ -61,7 +61,7 @@ Users (both direct and downstream) should be made aware of the risks, biases, an
 ### Training Data
-The model was fine-tuned on a dataset that includes various conversational data. These data were collected from my university website https://cdm.uowm.gr/en/index/.
 ### Training Procedure

 ### Model Description
+The Digital Navigator model is designed to assist users by generating natural language responses to input queries. It is a fine-tuned GPT-2 model, customized for providing  assistanse to visitors of my university's website: https://cdm.uowm.gr/en/index/.
 - **Developed by:** Papagiannakis Panagiotis
 - **Model type:** GPT-2 (Generative Pre-trained Transformer 2)
 ### Training Data
+The model was fine-tuned on a dataset that includes various conversational data. These data were collected from my university's website https://cdm.uowm.gr/en/index/.
 ### Training Procedure