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e9799d6821
The provided example uses the default `max_length` of `20` tokens, which leads to the example generation getting cut off. 20 tokens is way too short to show CoT reasoning, so I boosted it to `64`. Without knowing HF's API well, it can be hard to figure out just where those `model_kwargs` come from, and `max_length` is a super critical one.
72 lines
1.7 KiB
Plaintext
72 lines
1.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "959300d4",
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"metadata": {},
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"source": [
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"# Hugging Face Hub\n",
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"\n",
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"This example showcases how to connect to the Hugging Face Hub."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "3acf0069",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The Seattle Seahawks won the Super Bowl in 2010. Justin Beiber was born in 2010. The final answer: Seattle Seahawks.\n"
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]
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}
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],
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"source": [
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"from langchain import PromptTemplate, HuggingFaceHub, LLMChain\n",
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"\n",
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
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"llm_chain = LLMChain(prompt=prompt, llm=HuggingFaceHub(repo_id=\"google/flan-t5-xl\", model_kwargs={\"temperature\":0, \"max_length\":64}))\n",
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"\n",
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"question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
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"\n",
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"print(llm_chain.run(question))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ae4559c7",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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