Instructions to use preethiprabha2023/debug_seq2seq_trial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use preethiprabha2023/debug_seq2seq_trial with Transformers:

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

pipe = pipeline("text-generation", model="preethiprabha2023/debug_seq2seq_trial")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("preethiprabha2023/debug_seq2seq_trial")
model = AutoModelForSeq2SeqLM.from_pretrained("preethiprabha2023/debug_seq2seq_trial")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use preethiprabha2023/debug_seq2seq_trial with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "preethiprabha2023/debug_seq2seq_trial"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "preethiprabha2023/debug_seq2seq_trial",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/preethiprabha2023/debug_seq2seq_trial

SGLang

How to use preethiprabha2023/debug_seq2seq_trial 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 "preethiprabha2023/debug_seq2seq_trial" \
    --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": "preethiprabha2023/debug_seq2seq_trial",
		"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 "preethiprabha2023/debug_seq2seq_trial" \
        --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": "preethiprabha2023/debug_seq2seq_trial",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use preethiprabha2023/debug_seq2seq_trial with Docker Model Runner:
```
docker model run hf.co/preethiprabha2023/debug_seq2seq_trial
```

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

debug_seq2seq_trial

This model is a fine-tuned version of google/mt5-small on the preethiprabha2023/trial dataset. It achieves the following results on the evaluation set:

Loss: 15.5520

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e-05
train_batch_size: 12
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2.0

Training results

Framework versions

Transformers 4.38.0.dev0
Pytorch 2.2.0+cu121
Datasets 2.17.1
Tokenizers 0.15.2

Downloads last month: 5

Safetensors

Model size

0.3B params

Tensor type

F32

Model tree for preethiprabha2023/debug_seq2seq_trial

Base model

google/mt5-small

Finetuned

(676)

this model

preethiprabha2023
/

debug_seq2seq_trial

debug_seq2seq_trial

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for preethiprabha2023/debug_seq2seq_trial

Dataset used to train preethiprabha2023/debug_seq2seq_trial