Instructions to use pszemraj/t5e-mini-nl24-flan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pszemraj/t5e-mini-nl24-flan with Transformers:

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

pipe = pipeline("text-generation", model="pszemraj/t5e-mini-nl24-flan")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("pszemraj/t5e-mini-nl24-flan")
model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/t5e-mini-nl24-flan", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pszemraj/t5e-mini-nl24-flan with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "pszemraj/t5e-mini-nl24-flan"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pszemraj/t5e-mini-nl24-flan",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/pszemraj/t5e-mini-nl24-flan

SGLang

How to use pszemraj/t5e-mini-nl24-flan 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/t5e-mini-nl24-flan" \
    --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/t5e-mini-nl24-flan",
		"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/t5e-mini-nl24-flan" \
        --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/t5e-mini-nl24-flan",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pszemraj/t5e-mini-nl24-flan with Docker Model Runner:
```
docker model run hf.co/pszemraj/t5e-mini-nl24-flan
```

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

t5e-mini-nl24-flan

25k steps on FLAN as an initial test/validation that code works. Not practically useful.

from transformers import pipeline

pipe = pipeline(
    "text2text-generation",
    model="pszemraj/t5e-mini-nl24-flan",
)
res = pipe(
    "true or false: water is wet.",
    top_k=4,
    penalty_alpha=0.6,
    max_new_tokens=128,
)
print(res[0]["generated_text"])

Quick eval

Quick eval for: pszemraj/t5e-mini-nl24-flan

hf (pretrained=pszemraj/t5e-mini-nl24-flan,trust_remote_code=True,dtype=bfloat16,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 8

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
boolq	2	none	0	acc	↑	0.4541	±	0.0087
openbookqa	1	none	0	acc	↑	0.1300	±	0.0151
		none	0	acc_norm	↑	0.2700	±	0.0199
piqa	1	none	0	acc	↑	0.6159	±	0.0113
		none	0	acc_norm	↑	0.6077	±	0.0114
social_iqa	0	none	0	acc	↑	0.3705	±	0.0109
tinyArc	0	none	25	acc_norm	↑	0.2913	±	N/A
tinyGSM8k	0	flexible-extract	5	exact_match	↑	0.0269	±	N/A
		strict-match	5	exact_match	↑	0.0055	±	N/A
tinyHellaswag	0	none	10	acc_norm	↑	0.3538	±	N/A
tinyMMLU	0	none	0	acc_norm	↑	0.2551	±	N/A
winogrande	1	none	0	acc	↑	0.5217	±	0.0140

base model evals: click to expand

Quick eval for: google/t5-efficient-mini-nl24

hf (pretrained=google/t5-efficient-mini-nl24,trust_remote_code=True,dtype=bfloat16,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 8

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
boolq	2	none	0	acc	↑	0.3783	±	0.0085
openbookqa	1	none	0	acc	↑	0.1280	±	0.0150
		none	0	acc_norm	↑	0.2660	±	0.0198
piqa	1	none	0	acc	↑	0.5473	±	0.0116
		none	0	acc_norm	↑	0.5267	±	0.0116
social_iqa	0	none	0	acc	↑	0.3536	±	0.0108
tinyArc	0	none	25	acc_norm	↑	0.3101	±	N/A
tinyGSM8k	0	flexible-extract	5	exact_match	↑	0.0145	±	N/A
		strict-match	5	exact_match	↑	0.0055	±	N/A
tinyHellaswag	0	none	10	acc_norm	↑	0.2616	±	N/A
tinyMMLU	0	none	0	acc_norm	↑	0.2839	±	N/A
winogrande	1	none	0	acc	↑	0.4996	±	0.0141

Downloads last month: 8

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for pszemraj/t5e-mini-nl24-flan

Base model

google/t5-efficient-mini-nl24

Finetuned

(1)

this model

pszemraj
/

t5e-mini-nl24-flan

t5e-mini-nl24-flan

Quick eval

Model tree for pszemraj/t5e-mini-nl24-flan

Dataset used to train pszemraj/t5e-mini-nl24-flan