Instructions to use pszemraj/flan-t5-small-instructiongen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pszemraj/flan-t5-small-instructiongen with Transformers:

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

pipe = pipeline("text-generation", model="pszemraj/flan-t5-small-instructiongen")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("pszemraj/flan-t5-small-instructiongen")
model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/flan-t5-small-instructiongen")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pszemraj/flan-t5-small-instructiongen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pszemraj/flan-t5-small-instructiongen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pszemraj/flan-t5-small-instructiongen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pszemraj/flan-t5-small-instructiongen

SGLang

How to use pszemraj/flan-t5-small-instructiongen 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/flan-t5-small-instructiongen" \
    --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/flan-t5-small-instructiongen",
		"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/flan-t5-small-instructiongen" \
        --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/flan-t5-small-instructiongen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pszemraj/flan-t5-small-instructiongen with Docker Model Runner:
```
docker model run hf.co/pszemraj/flan-t5-small-instructiongen
```

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

flan-t5-small-instructiongen

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

This model is a fine-tuned version of google/flan-t5-small on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.3401
Rouge1: 52.201
Rouge2: 35.6154
Rougel: 50.2334
Rougelsum: 50.338
Gen Len: 14.0450

Intended uses & limitations

This is just a small model/example. There is likely to be even better performance with larger models (ex pszemraj/bart-base-instructiongen) generalizes better)

Additionally, this was trained on a dataset of only instructions+outputs, with the inputs filtered out. This means that text of 1) cookies and cream 2) chocolate chip 3) mint chip 4) oreo will not get you "Rank the following ice cream flavors: oreo, mint chip, chocolate chip, cookies and cream".

Training and evaluation data

See the linked dataset pszemraj/fleece2instructions - it is a filtered/formatted version of tatsu-lab/alpaca to generate instructions for arbitrary text.

Some of the API examples are intentionally weird to demonstrate the generalizability of the model.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 8e-05
train_batch_size: 8
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 16
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.02
num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
1.6161	1.0	181	1.3714	51.1003	34.5701	49.1277	49.2466	13.8357
1.539	2.0	362	1.3401	52.201	35.6154	50.2334	50.338	14.0450

Downloads last month: 16

Safetensors

Model size

77M params

Tensor type

F32

Dataset used to train pszemraj/flan-t5-small-instructiongen

Space using pszemraj/flan-t5-small-instructiongen 1

Evaluation results

Rouge1 on pszemraj/fleece2instructions
validation set self-reported

52.201