Instructions to use EleutherAI/pile-t5-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EleutherAI/pile-t5-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="EleutherAI/pile-t5-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pile-t5-base") model = AutoModelForSeq2SeqLM.from_pretrained("EleutherAI/pile-t5-base") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use EleutherAI/pile-t5-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "EleutherAI/pile-t5-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "EleutherAI/pile-t5-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/EleutherAI/pile-t5-base
- SGLang
How to use EleutherAI/pile-t5-base 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 "EleutherAI/pile-t5-base" \ --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": "EleutherAI/pile-t5-base", "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 "EleutherAI/pile-t5-base" \ --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": "EleutherAI/pile-t5-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use EleutherAI/pile-t5-base with Docker Model Runner:
docker model run hf.co/EleutherAI/pile-t5-base
Update README.md
Browse files
README.md
CHANGED
|
@@ -129,7 +129,7 @@ with the span-corruption objective.
|
|
| 129 |
|
| 130 |
Intermediate checkpoints for Pile-T5 are accessible within this repository.
|
| 131 |
There are in total 200 checkpoints that are spaced 10,000 steps. For T5x-native
|
| 132 |
-
checkpoints that can be used for finetuning with the T5x library, refer to [here](https://huggingface.co/lintang/pile-t5-base-t5x
|
| 133 |
|
| 134 |
The training loss (in tfevent format) and validation perplexity (in jsonl) can be found [here](https://huggingface.co/EleutherAI/pile-t5-base/blob/main/base.zip).
|
| 135 |
|
|
|
|
| 129 |
|
| 130 |
Intermediate checkpoints for Pile-T5 are accessible within this repository.
|
| 131 |
There are in total 200 checkpoints that are spaced 10,000 steps. For T5x-native
|
| 132 |
+
checkpoints that can be used for finetuning with the T5x library, refer to [here](https://huggingface.co/lintang/pile-t5-base-t5x)
|
| 133 |
|
| 134 |
The training loss (in tfevent format) and validation perplexity (in jsonl) can be found [here](https://huggingface.co/EleutherAI/pile-t5-base/blob/main/base.zip).
|
| 135 |
|