Instructions to use kaytoo2022/t5_technical_qa_082624 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kaytoo2022/t5_technical_qa_082624 with Transformers:

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

pipe = pipeline("text-generation", model="kaytoo2022/t5_technical_qa_082624")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("kaytoo2022/t5_technical_qa_082624")
model = AutoModelForSeq2SeqLM.from_pretrained("kaytoo2022/t5_technical_qa_082624")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kaytoo2022/t5_technical_qa_082624 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kaytoo2022/t5_technical_qa_082624"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kaytoo2022/t5_technical_qa_082624",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kaytoo2022/t5_technical_qa_082624

SGLang

How to use kaytoo2022/t5_technical_qa_082624 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 "kaytoo2022/t5_technical_qa_082624" \
    --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": "kaytoo2022/t5_technical_qa_082624",
		"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 "kaytoo2022/t5_technical_qa_082624" \
        --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": "kaytoo2022/t5_technical_qa_082624",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kaytoo2022/t5_technical_qa_082624 with Docker Model Runner:
```
docker model run hf.co/kaytoo2022/t5_technical_qa_082624
```

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

kaytoo2022/t5_technical_qa_082624

This model is a fine-tuned version of google/flan-t5-base on an unknown dataset. It achieves the following results on the evaluation set:

Train Loss: 1.5907
Validation Loss: 1.4118
Epoch: 14

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:

optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
training_precision: float32

Training results

Train Loss	Validation Loss	Epoch
3.0296	2.3499	0
2.5467	2.1372	1
2.3870	2.0202	2
2.2760	1.9289	3
2.1699	1.8520	4
2.1014	1.7799	5
2.0080	1.7177	6
1.9476	1.6605	7
1.8703	1.6072	8
1.8273	1.5629	9
1.7563	1.5233	10
1.7263	1.4896	11
1.6807	1.4582	12
1.6361	1.4327	13
1.5907	1.4118	14

Framework versions

Transformers 4.42.4
TensorFlow 2.17.0
Datasets 2.21.0
Tokenizers 0.19.1

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Model tree for kaytoo2022/t5_technical_qa_082624

Base model

google/flan-t5-base

Finetuned

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