Text Generation
Transformers
Safetensors
English
t5
text2text-generation
seq2seq
flan-t5
lightweight
grounded-qa
text-generation-inference
Instructions to use teapotai/tinyteapot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use teapotai/tinyteapot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="teapotai/tinyteapot")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("teapotai/tinyteapot") model = AutoModelForSeq2SeqLM.from_pretrained("teapotai/tinyteapot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use teapotai/tinyteapot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "teapotai/tinyteapot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "teapotai/tinyteapot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/teapotai/tinyteapot
- SGLang
How to use teapotai/tinyteapot 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 "teapotai/tinyteapot" \ --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": "teapotai/tinyteapot", "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 "teapotai/tinyteapot" \ --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": "teapotai/tinyteapot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use teapotai/tinyteapot with Docker Model Runner:
docker model run hf.co/teapotai/tinyteapot
Upload quantized ONNX model from best checkpoint
Browse files- decoder_model.onnx +3 -0
- decoder_with_past_model.onnx +3 -0
- encoder_model.onnx +3 -0
- ort_config.json +33 -0
- tokenizer.json +3 -12
- tokenizer_config.json +4 -0
decoder_model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:4a454bd6a5fbb181eb3b1594e96d1a80e393d7e141e1477cbc8a264dcb542599
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size 59049686
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decoder_with_past_model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:9bfb7d7a4916a419bc60ff00effa7a70df0ba087bfb35d251341231107f1606a
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size 55826418
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encoder_model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:e8c1ee28056e8e8c70373bc22c3584371c64d4c8c93f5f7e9be90fa79f4fc1b1
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size 35806614
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ort_config.json
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{
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"one_external_file": true,
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"opset": null,
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"optimization": {},
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"quantization": {
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"activations_dtype": "QUInt8",
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"activations_symmetric": false,
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"format": "QOperator",
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"is_static": false,
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"mode": "IntegerOps",
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"nodes_to_exclude": [],
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"nodes_to_quantize": [],
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"operators_to_quantize": [
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"Conv",
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"MatMul",
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"Attention",
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"LSTM",
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"Gather",
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"Transpose",
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"EmbedLayerNormalization"
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],
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"per_channel": true,
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"qdq_add_pair_to_weight": false,
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"qdq_dedicated_pair": false,
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"qdq_op_type_per_channel_support_to_axis": {
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"MatMul": 1
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},
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"reduce_range": false,
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"weights_dtype": "QInt8",
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"weights_symmetric": true
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},
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"use_external_data_format": false
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}
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tokenizer.json
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{
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"version": "1.0",
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"truncation": {
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"direction": "
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"max_length":
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"strategy": "LongestFirst",
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"stride": 0
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},
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"padding":
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"strategy": {
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"Fixed": 128
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},
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"direction": "Right",
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"pad_to_multiple_of": null,
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"pad_id": 0,
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"pad_type_id": 0,
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"pad_token": "<pad>"
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},
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"added_tokens": [
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{
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"id": 0,
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{
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"version": "1.0",
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"truncation": {
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"direction": "Right",
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"max_length": 512,
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"strategy": "LongestFirst",
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"stride": 0
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},
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"padding": null,
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"added_tokens": [
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{
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"id": 0,
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tokenizer_config.json
CHANGED
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"eos_token": "</s>",
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"extra_ids": 100,
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"extra_special_tokens": {},
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"model_max_length": 512,
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"pad_token": "<pad>",
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"sp_model_kwargs": {},
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"tokenizer_class": "T5Tokenizer",
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"unk_token": "<unk>"
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}
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"eos_token": "</s>",
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"extra_ids": 100,
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"extra_special_tokens": {},
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+
"max_length": 512,
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"model_max_length": 512,
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"pad_token": "<pad>",
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"sp_model_kwargs": {},
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"stride": 0,
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"tokenizer_class": "T5Tokenizer",
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"truncation_side": "right",
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"truncation_strategy": "longest_first",
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"unk_token": "<unk>"
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}
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