Instructions to use pszemraj/bart-base-open-instructiongen-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pszemraj/bart-base-open-instructiongen-v1 with Transformers:

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

pipe = pipeline("text-generation", model="pszemraj/bart-base-open-instructiongen-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("pszemraj/bart-base-open-instructiongen-v1")
model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/bart-base-open-instructiongen-v1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use pszemraj/bart-base-open-instructiongen-v1 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/pszemraj/bart-base-open-instructiongen-v1

SGLang

How to use pszemraj/bart-base-open-instructiongen-v1 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 "pszemraj/bart-base-open-instructiongen-v1" \
    --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": "pszemraj/bart-base-open-instructiongen-v1",
		"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 "pszemraj/bart-base-open-instructiongen-v1" \
        --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": "pszemraj/bart-base-open-instructiongen-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pszemraj/bart-base-open-instructiongen-v1 with Docker Model Runner:
```
docker model run hf.co/pszemraj/bart-base-open-instructiongen-v1
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

bart-base-open-instructiongen-v1

Instead of generating questions from text, generate instructions for LLMs!

Check out a basic demo on Spaces
An example of how to use instructiongen models in a CLI script can be found here
You can find other models fine-tuned for instruction generation by searching for the instructiongen tag

Model description

This model is a fine-tuned version of facebook/bart-base on the hakurei/open-instruct-v1 dataset.

This model only generates the instruction for arbitrary text (it does not provide inputs as well - look for models with w-inputs in the name).
There was no validation split at the time of training, so no statistics here.
Comparing the performance of this model with pszemraj/bart-base-instructiongen might give some indication of whether and how much dataset scaling is needed to produce "robust" instruction generators.
- If you notice any trends, feel free to reach out! would be happy to hear about it.

Training and evaluation data

See hakurei/open-instruct-v1. This model was trained on the dataset "backwards", i.e. the model was given the output column as input and trained to predict instruction.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 8e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.03
num_epochs: 2.0

Training results

Framework versions

Transformers 4.28.0.dev0
Pytorch 2.0.0+cu118
Datasets 2.9.0
Tokenizers 0.12.1

Downloads last month: 14

Safetensors

Model size

0.1B params

Tensor type

F32

pszemraj
/

bart-base-open-instructiongen-v1