Instructions to use iarroyof/t5-11b-ssm-nq-sharded with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iarroyof/t5-11b-ssm-nq-sharded with Transformers:

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

pipe = pipeline("text-generation", model="iarroyof/t5-11b-ssm-nq-sharded")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("iarroyof/t5-11b-ssm-nq-sharded")
model = AutoModelForSeq2SeqLM.from_pretrained("iarroyof/t5-11b-ssm-nq-sharded")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use iarroyof/t5-11b-ssm-nq-sharded with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "iarroyof/t5-11b-ssm-nq-sharded"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "iarroyof/t5-11b-ssm-nq-sharded",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/iarroyof/t5-11b-ssm-nq-sharded

SGLang

How to use iarroyof/t5-11b-ssm-nq-sharded 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 "iarroyof/t5-11b-ssm-nq-sharded" \
    --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": "iarroyof/t5-11b-ssm-nq-sharded",
		"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 "iarroyof/t5-11b-ssm-nq-sharded" \
        --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": "iarroyof/t5-11b-ssm-nq-sharded",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use iarroyof/t5-11b-ssm-nq-sharded with Docker Model Runner:
```
docker model run hf.co/iarroyof/t5-11b-ssm-nq-sharded
```

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

Model Description

This is a sharded version of the T5-11B-SSM-NQ model, fine-tuned on the Natural Questions dataset for text-to-text generation tasks. The model is stored and processed in multiple shards to facilitate easier handling of its large size (11 billion parameters).

Usage

This model can be used for text-to-text generation tasks like question answering and text summarization.

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained('iarroyof/t5-11b-ssm-nq-sharded')
model = AutoModelForSeq2SeqLM.from_pretrained(
    'iarroyof/t5-11b-ssm-nq-sharded',
    device_map='auto',
    low_cpu_mem_usage=True,
    torch_dtype=torch.float16,
)

inputs = tokenizer('What is and how to deal with insomnia?', return_tensors='pt').input_ids.to('cuda')
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
---

Downloads last month: 2

Safetensors

Model size

11B params

Tensor type

F32

F16

Model tree for iarroyof/t5-11b-ssm-nq-sharded

Base model

google/t5-11b-ssm-nq

Finetuned

(1)

this model

iarroyof
/

t5-11b-ssm-nq-sharded

Model Description

Usage

Model tree for iarroyof/t5-11b-ssm-nq-sharded

Dataset used to train iarroyof/t5-11b-ssm-nq-sharded