Instructions to use nlpcloud/instruct-gpt-j-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nlpcloud/instruct-gpt-j-fp16 with Transformers:

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

pipe = pipeline("text-generation", model="nlpcloud/instruct-gpt-j-fp16")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nlpcloud/instruct-gpt-j-fp16")
model = AutoModelForCausalLM.from_pretrained("nlpcloud/instruct-gpt-j-fp16")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nlpcloud/instruct-gpt-j-fp16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nlpcloud/instruct-gpt-j-fp16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nlpcloud/instruct-gpt-j-fp16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nlpcloud/instruct-gpt-j-fp16

SGLang

How to use nlpcloud/instruct-gpt-j-fp16 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 "nlpcloud/instruct-gpt-j-fp16" \
    --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": "nlpcloud/instruct-gpt-j-fp16",
		"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 "nlpcloud/instruct-gpt-j-fp16" \
        --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": "nlpcloud/instruct-gpt-j-fp16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nlpcloud/instruct-gpt-j-fp16 with Docker Model Runner:
```
docker model run hf.co/nlpcloud/instruct-gpt-j-fp16
```

Few shot learning

by iRanadheer - opened Mar 19, 2023

Discussion

iRanadheer

Mar 19, 2023

Hey, finetuning the GPTJ with Stanford alpaca instructions is a great idea, I'm looking for something like this, it's great to see someone has already done a great job.

I'm trying to do few-shot learning (classification) with the GPT-J model, but it doesn't seem to do a good job. I have tried the alpaca model (LORA), and it has increased the accuracy, but it's still not enough. So, I've come across your instruct-gptj model. I tried the few-shot learning, it doesn't seem to understand few-shot learning now, does it? I got very bad results all over. Maybe I'm doing something completely wrong. I was looking for the documentation for this model and it says that I don't need the few-shot learning as the model can understand the instructions.

what if I want to do the few-shot and provide a few examples for each class? will it work? Do you have any sample prompts? or have you not tested this? I'm very curious to know.

juliensalinas

NLP Cloud org Mar 20, 2023

Thanks, and cool to see that you had the same idea :)
In our tests this model seems to still perform correctly for few-shot learning, even if fine-tuned for instructions. For to be honest I don't really see why you would use this model if you are trying to perform text classification with few-shot learning.
Maybe you can have a look at our guide to see how to perform few-shot learning for classification: https://nlpcloud.com/effectively-using-gpt-j-gpt-neo-gpt-3-alternatives-few-shot-learning.html#zero-shot-text-classification

iRanadheer

Mar 20, 2023

I have tried GPT-J and NEO for few-shot learning. the quality is not good enough. In fact, I tested a few examples on your playground, I think it's more or less the same. Did you retrain or fine-tune the gptj model before you create an API?

juliensalinas

NLP Cloud org Mar 21, 2023

"I think it's more or less the same" --> I am not exactly sure what you mean by that.
Maybe you can copy paste a few-shot example here so I can advise, and so it benefits everyone who is reading this?

"Did you retrain or fine-tune the gptj model" --> Yes this instruct GPT-J model is a fine-tuned version of GPT-J

silvacarl

Mar 21, 2023

if you want to see something insane, try nlpcloud's GPT_Neo fien tuned mdoel.

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