Instructions to use VMware/flan-ul2-alpaca-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VMware/flan-ul2-alpaca-lora with Transformers:

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

pipe = pipeline("text-generation", model="VMware/flan-ul2-alpaca-lora")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("VMware/flan-ul2-alpaca-lora")
model = AutoModelForSeq2SeqLM.from_pretrained("VMware/flan-ul2-alpaca-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use VMware/flan-ul2-alpaca-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VMware/flan-ul2-alpaca-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VMware/flan-ul2-alpaca-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/VMware/flan-ul2-alpaca-lora

SGLang

How to use VMware/flan-ul2-alpaca-lora 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 "VMware/flan-ul2-alpaca-lora" \
    --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": "VMware/flan-ul2-alpaca-lora",
		"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 "VMware/flan-ul2-alpaca-lora" \
        --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": "VMware/flan-ul2-alpaca-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use VMware/flan-ul2-alpaca-lora with Docker Model Runner:
```
docker model run hf.co/VMware/flan-ul2-alpaca-lora
```

Teja-Gollapudi commited on Jun 8, 2023

Commit

cbf40e1

1 Parent(s): 3f5cf16

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -1

README.md CHANGED Viewed

@@ -22,7 +22,9 @@ license: other
 We take the instruction-tuned Flan models (trained on Academic datasets) and perform style transfer using the Alpaca dataset.
-We released the code for LORA fine-tuning Seq2Seq models in our medium article here: https://medium.com/vmware-data-ml-blog/lora-finetunning-of-ul-2-and-t5-models-35a08863593d
 We fine-tuned the `google/flan-ul2` model on the Alpaca dataset using [PEFT-LORA](https://huggingface.co/docs/diffusers/main/en/training/lora).

 We take the instruction-tuned Flan models (trained on Academic datasets) and perform style transfer using the Alpaca dataset.
+We released the code for LORA fine-tuning Seq2Seq models along with code walkthrough medium article here:
+- https://github.com/vmware-labs/research-and-development-artificial-intelligence-lab/tree/main/instruction-tuning/peft-seq2seq
+- https://medium.com/vmware-data-ml-blog/lora-finetunning-of-ul-2-and-t5-models-35a08863593d
 We fine-tuned the `google/flan-ul2` model on the Alpaca dataset using [PEFT-LORA](https://huggingface.co/docs/diffusers/main/en/training/lora).