Instructions to use crumb/gpt-j-6b-finetune-super-glue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use crumb/gpt-j-6b-finetune-super-glue with Transformers:

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

pipe = pipeline("text-generation", model="crumb/gpt-j-6b-finetune-super-glue")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("crumb/gpt-j-6b-finetune-super-glue")
model = AutoModelForMultimodalLM.from_pretrained("crumb/gpt-j-6b-finetune-super-glue")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use crumb/gpt-j-6b-finetune-super-glue with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "crumb/gpt-j-6b-finetune-super-glue"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "crumb/gpt-j-6b-finetune-super-glue",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/crumb/gpt-j-6b-finetune-super-glue

SGLang

How to use crumb/gpt-j-6b-finetune-super-glue 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 "crumb/gpt-j-6b-finetune-super-glue" \
    --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": "crumb/gpt-j-6b-finetune-super-glue",
		"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 "crumb/gpt-j-6b-finetune-super-glue" \
        --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": "crumb/gpt-j-6b-finetune-super-glue",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use crumb/gpt-j-6b-finetune-super-glue with Docker Model Runner:
```
docker model run hf.co/crumb/gpt-j-6b-finetune-super-glue
```

gpt-j-6b-finetune-super-glue / README.md

mia naomi

Update README.md

f44bace almost 4 years ago

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metadata

language:
  - en
datasets:
  - The Pile
  - super_glue
tags:
  - pytorch
  - causal-lm
  - 8bit
inference: false

side project, not final working good product

see this repo for more information, finetuned with 8-bit Adam on custom transformed super glue tasks, transformed somewhat like the T0 paper

from IPython import display 
!pip install transformers==4.14.1 -q
!pip install bitsandbytes-cuda111==0.26.0 -q
!pip install git+https://github.com/aicrumb/transformers-8bit -q

import transformers_8bit

model, tokenizer, config = transformers_8bit.gptj("crumb/gpt-j-6b-finetune-super-glue", device='cuda')

prompt = tokenizer("<QUERY> If birds are in group B, and snakes are in group A, what group are pythons in? <RESPONSE>", return_tensors='pt')
prompt = {key: value.to('cuda') for key, value in prompt.items()}
length = len(prompt['input_ids'][0])
out = model.generate(**prompt, min_length=length+2, max_length=length+2, do_sample=False, pad_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(out[0]))

"""output
<QUERY> If birds are in group B, and snakes are in group A, what group are pythons in? <RESPONSE> Group A
"""