Instructions to use kaytoo2022/t5_technical_qa_with_react_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kaytoo2022/t5_technical_qa_with_react_3 with Transformers:

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

pipe = pipeline("text-generation", model="kaytoo2022/t5_technical_qa_with_react_3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("kaytoo2022/t5_technical_qa_with_react_3")
model = AutoModelForSeq2SeqLM.from_pretrained("kaytoo2022/t5_technical_qa_with_react_3")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kaytoo2022/t5_technical_qa_with_react_3 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_with_react_3"
# 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_with_react_3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kaytoo2022/t5_technical_qa_with_react_3

SGLang

How to use kaytoo2022/t5_technical_qa_with_react_3 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_with_react_3" \
    --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_with_react_3",
		"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_with_react_3" \
        --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_with_react_3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kaytoo2022/t5_technical_qa_with_react_3 with Docker Model Runner:
```
docker model run hf.co/kaytoo2022/t5_technical_qa_with_react_3
```

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_with_react_3

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: 1.1006
Validation Loss: 1.6608
Epoch: 19

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
2.5832	2.2541	0
2.2701	2.1489	1
2.1253	2.0771	2
2.0259	2.0242	3
1.9282	1.9847	4
1.8461	1.9378	5
1.7743	1.9069	6
1.7029	1.8796	7
1.6345	1.8492	8
1.5830	1.8286	9
1.5227	1.8002	10
1.4597	1.7731	11
1.4119	1.7558	12
1.3577	1.7440	13
1.3038	1.7274	14
1.2705	1.7083	15
1.2188	1.6981	16
1.1836	1.6850	17
1.1456	1.6737	18
1.1006	1.6608	19

Framework versions

Transformers 4.42.4
TensorFlow 2.17.0
Datasets 2.20.0
Tokenizers 0.19.1

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Model tree for kaytoo2022/t5_technical_qa_with_react_3

Base model

google/flan-t5-base

Finetuned

(920)

this model

Finetunes

1 model