Instructions to use monsoon-nlp/gpt-nyc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use monsoon-nlp/gpt-nyc with Transformers:

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

pipe = pipeline("text-generation", model="monsoon-nlp/gpt-nyc")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("monsoon-nlp/gpt-nyc")
model = AutoModelForCausalLM.from_pretrained("monsoon-nlp/gpt-nyc")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use monsoon-nlp/gpt-nyc with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/monsoon-nlp/gpt-nyc

SGLang

How to use monsoon-nlp/gpt-nyc 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 "monsoon-nlp/gpt-nyc" \
    --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": "monsoon-nlp/gpt-nyc",
		"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 "monsoon-nlp/gpt-nyc" \
        --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": "monsoon-nlp/gpt-nyc",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use monsoon-nlp/gpt-nyc with Docker Model Runner:
```
docker model run hf.co/monsoon-nlp/gpt-nyc
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GPT-NYC

About

GPT2-Medium fine-tuned on questions and responses from https://reddit.com/r/asknyc

2023 Update: try a larger model: monsoon-nlp/nyc-savvy-llama2-7b

I filtered comments to ones with scores >= 3, and responding directly to the original post ( = ignoring responses to other commenters).

I added tokens to match NYC neighborhoods, subway stations, foods, and other common terms in the original batches of questions and comments. You would be surprised what is missing from GPT tokens!

Try prompting with question? %% or question? - more info %%