Instructions to use jkim03/rendezvous-radar-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jkim03/rendezvous-radar-model with Transformers:

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

pipe = pipeline("text-generation", model="jkim03/rendezvous-radar-model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jkim03/rendezvous-radar-model")
model = AutoModelForCausalLM.from_pretrained("jkim03/rendezvous-radar-model")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jkim03/rendezvous-radar-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jkim03/rendezvous-radar-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jkim03/rendezvous-radar-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jkim03/rendezvous-radar-model

SGLang

How to use jkim03/rendezvous-radar-model 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 "jkim03/rendezvous-radar-model" \
    --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": "jkim03/rendezvous-radar-model",
		"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 "jkim03/rendezvous-radar-model" \
        --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": "jkim03/rendezvous-radar-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jkim03/rendezvous-radar-model with Docker Model Runner:
```
docker model run hf.co/jkim03/rendezvous-radar-model
```

Model Card for Model ID

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: [Jinha Kim and Jerry Chen]
Model type: [LLM Prompt Classifier]
License: [MIT]
Finetuned from model [distilgpt2]: [https://huggingface.co/distilbert/distilgpt2]

Repository: [https://huggingface.co/jkim03/rendezvous-radar-model]

Uses

Used to return OpenStreetMap tags from user prompts.

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.

Training Details

Training Data

Training data

Training Procedure and Hyperparameters

Training Script

Speeds, Sizes, Times [optional]

Training runtime: 774.4637 Training samples per second: 1.704 Training steps per second: 0.857

Evaluation

Training Loss: 1.234485605394984 Epoch: 8.0 Loss: 0.3482

Downloads last month: 3

Safetensors

Model size

81.9M params

Tensor type

F32