mirror of
https://github.com/norandom/project_bookworm.git
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256 lines
9.1 KiB
Plaintext
256 lines
9.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "ea47b0b7196331ed",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"# Use a local CPU Large Language Model (LLM) to generate text\n",
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"\n",
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"This is a basic LLM, which \n",
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"\n",
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"* does not require a GPU\n",
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"* is not fine-tuned for a specific task\n",
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"* is not optimized for speed\n",
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"* is not optimized for memory usage\n",
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"* has a smaller model size\n",
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"* ...\n",
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"* is not as good as a GPU LLM\n",
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"* is not as good as a fine-tuned LLM\n",
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"* is not as good as a larger LLM\n",
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"* ...\n",
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"\n",
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"Its purpose is to allow on-premises and self-hosted use of LLMs. "
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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": 5,
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"id": "initial_id",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-03-17T11:26:30.714741Z",
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"start_time": "2024-03-17T11:26:30.711615Z"
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},
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# You need to manage the dependencies of LangChain with\n",
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"# the requirements.txt file. The versions are pinned.\n",
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"# %pip install -r requirements.txt"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c96a287c1fc724d2",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"## Use the Hugging Face pipeline with LLMware Bling\n",
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"\n",
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"* The Hugging Face pipeline is a convenient way to use a pre-trained model.\n",
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"* LLMware Bling is a CPU LLM.\n",
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"* The config of this model is to allow remote code from Hugging Face."
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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": 2,
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"id": "2108b1c9373e0ec8",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-03-17T11:23:02.052134Z",
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"start_time": "2024-03-17T11:22:45.974223Z"
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},
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"loading file vocab.json from cache at None\n",
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"loading file merges.txt from cache at None\n",
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"loading file tokenizer.json from cache at /home/marius/.cache/huggingface/hub/models--llmware--bling-stable-lm-3b-4e1t-v0/snapshots/a9e4d8d478d76dd062d9acd01b6ce3417217a344/tokenizer.json\n",
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"loading file added_tokens.json from cache at None\n",
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"loading file special_tokens_map.json from cache at /home/marius/.cache/huggingface/hub/models--llmware--bling-stable-lm-3b-4e1t-v0/snapshots/a9e4d8d478d76dd062d9acd01b6ce3417217a344/special_tokens_map.json\n",
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"loading file tokenizer_config.json from cache at /home/marius/.cache/huggingface/hub/models--llmware--bling-stable-lm-3b-4e1t-v0/snapshots/a9e4d8d478d76dd062d9acd01b6ce3417217a344/tokenizer_config.json\n",
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"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
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"loading configuration file config.json from cache at /home/marius/.cache/huggingface/hub/models--llmware--bling-stable-lm-3b-4e1t-v0/snapshots/a9e4d8d478d76dd062d9acd01b6ce3417217a344/config.json\n",
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"loading configuration file config.json from cache at /home/marius/.cache/huggingface/hub/models--llmware--bling-stable-lm-3b-4e1t-v0/snapshots/a9e4d8d478d76dd062d9acd01b6ce3417217a344/config.json\n",
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"Model config StableLMEpochConfig {\n",
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" \"_name_or_path\": \"llmware/bling-stable-lm-3b-4e1t-v0\",\n",
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" \"architectures\": [\n",
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" \"StableLMEpochForCausalLM\"\n",
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" ],\n",
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" \"auto_map\": {\n",
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" \"AutoConfig\": \"llmware/bling-stable-lm-3b-4e1t-v0--configuration_stablelm_epoch.StableLMEpochConfig\",\n",
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" \"AutoModelForCausalLM\": \"llmware/bling-stable-lm-3b-4e1t-v0--modeling_stablelm_epoch.StableLMEpochForCausalLM\"\n",
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" },\n",
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" \"bos_token_id\": 0,\n",
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" \"eos_token_id\": 0,\n",
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" \"hidden_act\": \"silu\",\n",
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" \"hidden_size\": 2560,\n",
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" \"initializer_range\": 0.02,\n",
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" \"intermediate_size\": 6912,\n",
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" \"max_position_embeddings\": 4096,\n",
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" \"model_type\": \"stablelm_epoch\",\n",
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" \"norm_eps\": 1e-05,\n",
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" \"num_attention_heads\": 32,\n",
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" \"num_heads\": 32,\n",
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" \"num_hidden_layers\": 32,\n",
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" \"num_key_value_heads\": 32,\n",
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" \"rope_pct\": 0.25,\n",
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" \"rope_theta\": 10000,\n",
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" \"rotary_scaling_factor\": 1.0,\n",
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" \"tie_word_embeddings\": false,\n",
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" \"torch_dtype\": \"bfloat16\",\n",
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" \"transformers_version\": \"4.38.2\",\n",
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" \"use_cache\": true,\n",
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" \"vocab_size\": 50304\n",
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"}\n",
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"\n",
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"loading weights file pytorch_model.bin from cache at /home/marius/.cache/huggingface/hub/models--llmware--bling-stable-lm-3b-4e1t-v0/snapshots/a9e4d8d478d76dd062d9acd01b6ce3417217a344/pytorch_model.bin\n",
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"Generate config GenerationConfig {\n",
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" \"bos_token_id\": 0,\n",
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" \"eos_token_id\": 0\n",
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"}\n",
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"\n",
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"All model checkpoint weights were used when initializing StableLMEpochForCausalLM.\n",
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"\n",
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"All the weights of StableLMEpochForCausalLM were initialized from the model checkpoint at llmware/bling-stable-lm-3b-4e1t-v0.\n",
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"If your task is similar to the task the model of the checkpoint was trained on, you can already use StableLMEpochForCausalLM for predictions without further training.\n",
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"loading configuration file generation_config.json from cache at /home/marius/.cache/huggingface/hub/models--llmware--bling-stable-lm-3b-4e1t-v0/snapshots/a9e4d8d478d76dd062d9acd01b6ce3417217a344/generation_config.json\n",
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"Generate config GenerationConfig {\n",
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" \"bos_token_id\": 0,\n",
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" \"eos_token_id\": 0\n",
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"}\n",
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"\n"
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]
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}
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],
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"source": [
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"from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline\n",
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"from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
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"\n",
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"model_id = \"llmware/bling-stable-lm-3b-4e1t-v0\"\n",
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"\n",
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"# Ensure the directory for saving models is created and specified in your environment\n",
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"# This is more about ensuring that the model download doesn't prompt for storage location or confirmation\n",
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"import os\n",
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"from transformers import logging\n",
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"\n",
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"# Optionally, increase logging level if you want to see more details about the download process\n",
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"logging.set_verbosity_info()\n",
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"\n",
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"# Make sure you have set TRANSFORMERS_CACHE in your environment variables\n",
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"# os.environ[\"TRANSFORMERS_CACHE\"] = \"/path/to/your/preferred/cache/directory\"\n",
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"\n",
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"tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
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"model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)\n",
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"\n",
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"pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=500)\n",
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"hf = HuggingFacePipeline(pipeline=pipe)\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "904e6bf72c2ecf27",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"## Use the Hugging Face pipeline with LLMware Bling via LangChain\n",
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"\n",
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"* This is a basic prompt template with LangChain\n",
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"* The question is passed to the model via a chain"
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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": "1827b8c3423066b0",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-03-17T11:23:25.839334Z",
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"start_time": "2024-03-17T11:23:02.070024Z"
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},
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Disabling tokenizer parallelism, we're using DataLoader multithreading already\n",
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"Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
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]
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}
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],
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"source": [
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"from langchain.prompts import PromptTemplate\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.from_template(template)\n",
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"\n",
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"chain = prompt | hf\n",
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"\n",
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"question = \"What is electroencephalography?\"\n",
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"\n",
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"test = chain.invoke({\"question\": 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": 4,
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"id": "ac2a19b6fb9aa3e2",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-03-17T11:23:25.847308Z",
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"start_time": "2024-03-17T11:23:25.841002Z"
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},
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"collapsed": false
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},
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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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" First, electroencephalography (EEG) is a medical test that measures electrical activity in the brain. Second, EEG is a type of electrodiagnostic test. Third, electrodiagnostic tests are used to evaluate neurological conditions.\n"
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]
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}
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],
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"source": [
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"print(test)"
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]
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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",
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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": 2
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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": "ipython2",
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"version": "2.7.6"
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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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