Create README.md

README.md

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+---
+library_name: "transformers.js"
+base_model: philschmid/flan-t5-base-samsum
+license: apache-2.0
+datasets:
+- samsum
+---
+
+INT8 ONNX version of [philschmid/flan-t5-base-samsum](https://huggingface.co/philschmid/flan-t5-base-samsum) to use with [Transformers.js](https://huggingface.co/docs/transformers.js).
+
+### Example usage
+#### Pipeline API
+```js
+import { pipeline } from '@xenova/transformers';
+
+const generator = await pipeline('text-generation', 'Felladrin/onnx-flan-t5-base-samsum');
+const output =  await generator("Val: it's raining! Candy: I know, just started... Val: r we going? we will be wet Candy: maybe wait a little? see if stops Val: ok. let's wait half h and than see Candy: god idea, I call u then Val: great :)", { add_special_tokens: true, max_new_tokens: 60, repetition_penalty: 1.2});
+console.log(output);
+// It's raining. Val and Candy will wait half an hour and then see if...
+```
+
+#### Auto Classes
+```js
+import { AutoModelForCausalLM, AutoTokenizer } from '@xenova/transformers';
+
+const model_path = 'Felladrin/onnx-flan-t5-base-samsum';
+const model = await AutoModelForCausalLM.from_pretrained(model_path);
+const tokenizer = await AutoTokenizer.from_pretrained(model_path);
+
+const prompt = "Val: it's raining! Candy: I know, just started... Val: r we going? we will be wet Candy: maybe wait a little? see if stops Val: ok. let's wait half h and than see Candy: god idea, I call u then Val: great :)";
+const { input_ids } = tokenizer(prompt);
+const tokens = await model.generate(input_ids, { max_new_tokens: 60, repetition_penalty: 1.2});
+console.log(tokenizer.decode(tokens[0], { skip_special_tokens: true }));
+// It's raining. Val and Candy will wait half an hour and then see if...
+```
+