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sebastianschramm/ner_with_uniner-7b-type-gptq-4bit.ipynb

Created August 27, 2023 11:15
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},
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"<a href=\"https://colab.research.google.com/gist/sebastianschramm/2ef835fb6ba9fa54c0c7bfda9022700f/ner_with_uniner-7b-type-gptq-4bit.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
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"execution_count": 1,
"metadata": {
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},
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"text": [
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
" Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
" Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m251.2/251.2 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for optimum (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
]
}
],
"source": [
"!pip -q install accelerate auto-gptq git+https://github.com/huggingface/optimum.git git+https://github.com/huggingface/transformers.git"
]
},
{
"cell_type": "code",
"source": [
"import json\n",
"\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline"
],
"metadata": {
"id": "ueqiy4GU1wqO"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model_name = \"SebastianSchramm/UniNER-7B-type-GPTQ-4bit-128g-actorder_True\"\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
"model = AutoModelForCausalLM.from_pretrained(model_name, device_map=\"auto\")"
],
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"outputs": [
{
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"data": {
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},
{
"cell_type": "code",
"source": [
"pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=50, return_full_text=False)"
],
"metadata": {
"id": "WYTz529FFZV0"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"prompt_template = \"\"\"A virtual assistant answers questions from a user based on the provided text.\n",
"USER: Text: {input_text}\n",
"ASSISTANT: I’ve read this text.\n",
"USER: What describes {entity_name} in the text?\n",
"ASSISTANT:\n",
"\"\"\""
],
"metadata": {
"id": "QIVaAMvEVRL5"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"prompt = prompt_template.format_map({\"input_text\": \"Cologne is a great city in Germany - maybe even the greatest ;)\", \"entity_name\": \"city\"})"
],
"metadata": {
"id": "k5xr9oPGVmWS"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"json.loads(pipe(prompt)[0]['generated_text'])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fkfK8SQ0WKtx",
"outputId": "bb9e6c3d-ded5-4204-dd95-9059e4736ef3"
},
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['Cologne']"
]
},
"metadata": {},
"execution_count": 8
}
]
}
]
}
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