Instructions to use Isotonic/flan-t5-base-trading_candles with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Isotonic/flan-t5-base-trading_candles with Transformers:

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

pipe = pipeline("text-generation", model="Isotonic/flan-t5-base-trading_candles")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Isotonic/flan-t5-base-trading_candles")
model = AutoModelForSeq2SeqLM.from_pretrained("Isotonic/flan-t5-base-trading_candles")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Isotonic/flan-t5-base-trading_candles with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Isotonic/flan-t5-base-trading_candles

SGLang

How to use Isotonic/flan-t5-base-trading_candles 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 "Isotonic/flan-t5-base-trading_candles" \
    --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": "Isotonic/flan-t5-base-trading_candles",
		"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 "Isotonic/flan-t5-base-trading_candles" \
        --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": "Isotonic/flan-t5-base-trading_candles",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Isotonic/flan-t5-base-trading_candles with Docker Model Runner:
```
docker model run hf.co/Isotonic/flan-t5-base-trading_candles
```

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

flan-t5-base-trading_candles

This model is a fine-tuned version of google/flan-t5-base on 0xMaka/trading-candles-subset-qa-format dataset. It achieves the following results on the evaluation set:

Loss: 0.0061
Rouge1: 88.3665
Rouge2: 86.86
Rougel: 88.3651
Rougelsum: 88.3665
Gen Len: 18.9025

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: 4
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
0.019	1.0	70009	0.0089	88.0774	86.4734	88.0734	88.0748	18.9022
0.0095	2.0	140018	0.0069	88.3636	86.8542	88.3612	88.3625	18.9016
0.0071	3.0	210027	0.0061	88.3665	86.86	88.3651	88.3665	18.9025

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3

Downloads last month: 12

Safetensors

Model size

0.2B params

Tensor type

F32

Model tree for Isotonic/flan-t5-base-trading_candles

Base model

google/flan-t5-base

Finetuned

(913)

this model

Adapters

1 model

Isotonic
/

flan-t5-base-trading_candles

flan-t5-base-trading_candles

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for Isotonic/flan-t5-base-trading_candles

Dataset used to train Isotonic/flan-t5-base-trading_candles

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