Spaces:
Sleeping
Sleeping
File size: 4,662 Bytes
cfd3735 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 |
{
"cells": [
{
"cell_type": "markdown",
"id": "eec4efda",
"metadata": {},
"source": [
"# Self Hosted Embeddings\n",
"Let's load the SelfHostedEmbeddings, SelfHostedHuggingFaceEmbeddings, and SelfHostedHuggingFaceInstructEmbeddings classes."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d338722a",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"from langchain.embeddings import (\n",
" SelfHostedEmbeddings,\n",
" SelfHostedHuggingFaceEmbeddings,\n",
" SelfHostedHuggingFaceInstructEmbeddings,\n",
")\n",
"import runhouse as rh"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "146559e8",
"metadata": {},
"outputs": [],
"source": [
"# For an on-demand A100 with GCP, Azure, or Lambda\n",
"gpu = rh.cluster(name=\"rh-a10x\", instance_type=\"A100:1\", use_spot=False)\n",
"\n",
"# For an on-demand A10G with AWS (no single A100s on AWS)\n",
"# gpu = rh.cluster(name='rh-a10x', instance_type='g5.2xlarge', provider='aws')\n",
"\n",
"# For an existing cluster\n",
"# gpu = rh.cluster(ips=['<ip of the cluster>'],\n",
"# ssh_creds={'ssh_user': '...', 'ssh_private_key':'<path_to_key>'},\n",
"# name='my-cluster')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1230f7df",
"metadata": {},
"outputs": [],
"source": [
"embeddings = SelfHostedHuggingFaceEmbeddings(hardware=gpu)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2684e928",
"metadata": {},
"outputs": [],
"source": [
"text = \"This is a test document.\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1dc5e606",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"query_result = embeddings.embed_query(text)"
]
},
{
"cell_type": "markdown",
"id": "cef9cc54",
"metadata": {},
"source": [
"And similarly for SelfHostedHuggingFaceInstructEmbeddings:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "81a17ca3",
"metadata": {},
"outputs": [],
"source": [
"embeddings = SelfHostedHuggingFaceInstructEmbeddings(hardware=gpu)"
]
},
{
"cell_type": "markdown",
"id": "5a33d1c8",
"metadata": {},
"source": [
"Now let's load an embedding model with a custom load function:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "c4af5679",
"metadata": {},
"outputs": [],
"source": [
"def get_pipeline():\n",
" from transformers import (\n",
" AutoModelForCausalLM,\n",
" AutoTokenizer,\n",
" pipeline,\n",
" ) # Must be inside the function in notebooks\n",
"\n",
" model_id = \"facebook/bart-base\"\n",
" tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
" model = AutoModelForCausalLM.from_pretrained(model_id)\n",
" return pipeline(\"feature-extraction\", model=model, tokenizer=tokenizer)\n",
"\n",
"\n",
"def inference_fn(pipeline, prompt):\n",
" # Return last hidden state of the model\n",
" if isinstance(prompt, list):\n",
" return [emb[0][-1] for emb in pipeline(prompt)]\n",
" return pipeline(prompt)[0][-1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8654334b",
"metadata": {},
"outputs": [],
"source": [
"embeddings = SelfHostedEmbeddings(\n",
" model_load_fn=get_pipeline,\n",
" hardware=gpu,\n",
" model_reqs=[\"./\", \"torch\", \"transformers\"],\n",
" inference_fn=inference_fn,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fc1bfd0f",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"query_result = embeddings.embed_query(text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aaad49f8",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.1"
},
"vscode": {
"interpreter": {
"hash": "7377c2ccc78bc62c2683122d48c8cd1fb85a53850a1b1fc29736ed39852c9885"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|