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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

debug_seq2seq_trial / README.md

preethiprabha2023

Update README.md

30f3b1e verified over 2 years ago

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metadata

license: apache-2.0
base_model: google/mt5-small
tags:
  - generated_from_trainer
datasets:
  - preethiprabha2023/trial
model-index:
  - name: debug_seq2seq_trial
    results: []
language:
  - ta
metrics: null
pipeline_tag: text2text-generation
widget:
  - text: வெறுப்பு, காழ்ப்பு, கசப்பு, மனசகப்பு, எரிச்சல்.
    context: துக்கம், இழப்பு, கவலை முதலியவற்றால் தன் மேல் ஏற்படும் வெறுப்பு.
    example_title: Feelings

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