Instructions to use li-jay-cs/gptj-supervised-summarize-checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use li-jay-cs/gptj-supervised-summarize-checkpoint with Transformers:

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

pipe = pipeline("text-generation", model="li-jay-cs/gptj-supervised-summarize-checkpoint")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("li-jay-cs/gptj-supervised-summarize-checkpoint")
model = AutoModelForCausalLM.from_pretrained("li-jay-cs/gptj-supervised-summarize-checkpoint")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use li-jay-cs/gptj-supervised-summarize-checkpoint with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/li-jay-cs/gptj-supervised-summarize-checkpoint

SGLang

How to use li-jay-cs/gptj-supervised-summarize-checkpoint 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 "li-jay-cs/gptj-supervised-summarize-checkpoint" \
    --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": "li-jay-cs/gptj-supervised-summarize-checkpoint",
		"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 "li-jay-cs/gptj-supervised-summarize-checkpoint" \
        --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": "li-jay-cs/gptj-supervised-summarize-checkpoint",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use li-jay-cs/gptj-supervised-summarize-checkpoint with Docker Model Runner:
```
docker model run hf.co/li-jay-cs/gptj-supervised-summarize-checkpoint
```

li-jay-cs commited on Nov 13, 2023

Commit

15612e6

1 Parent(s): f2d8664

Model save

Browse files

Files changed (4) hide show

README.md +2 -2
config.json +1 -1
runs/Nov13_00-55-24_d816b4927aca/events.out.tfevents.1699836935.d816b4927aca.7494.0 +3 -0
training_args.bin +1 -1

README.md CHANGED Viewed

@@ -1,6 +1,6 @@
 ---
 license: mit
-base_model: gpt2
 tags:
 - generated_from_trainer
 model-index:
@@ -13,7 +13,7 @@ should probably proofread and complete it, then remove this comment. -->
 # gptj-supervised-summarize-checkpoint
-This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
 ## Model description

 ---
 license: mit
+base_model: li-jay-cs/gptj-supervised-summarize-checkpoint
 tags:
 - generated_from_trainer
 model-index:
 # gptj-supervised-summarize-checkpoint
+This model is a fine-tuned version of [li-jay-cs/gptj-supervised-summarize-checkpoint](https://huggingface.co/li-jay-cs/gptj-supervised-summarize-checkpoint) on an unknown dataset.
 ## Model description

config.json CHANGED Viewed

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "gpt2",
   "activation_function": "gelu_new",
   "architectures": [
     "GPT2LMHeadModel"

 {
+  "_name_or_path": "li-jay-cs/gptj-supervised-summarize-checkpoint",
   "activation_function": "gelu_new",
   "architectures": [
     "GPT2LMHeadModel"

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