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{ | |
"cell_type": "markdown", | |
"metadata": { | |
"id": "view-in-github", | |
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"<a href=\"https://colab.research.google.com/gist/sebastianschramm/b849c06676c6601d9a87270e83f5a157/ner_with_uniner-7b-definition-gptq-4bit.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"colab": { | |
"base_uri": "https://localhost:8080/" | |
}, | |
"id": "Pz-9Qh3f1otr", | |
"outputId": "101cf8cd-2f72-4fef-ffb9-e4d97f7a10fd" | |
}, | |
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{ | |
"output_type": "stream", | |
"name": "stdout", | |
"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[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | |
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m13.0 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[31m20.0 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[31m15.3 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-definition-GPTQ-4bit-128g-actorder_True\"\n", | |
"\n", | |
"tokenizer = AutoTokenizer.from_pretrained(model_name)\n", | |
"model = AutoModelForCausalLM.from_pretrained(model_name, device_map=\"auto\")" | |
], | |
"metadata": { | |
"id": "Dbf7_AkR2ApV", | |
"colab": { | |
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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": "532c1a18-0026-4207-9487-793d9ca4ca46" | |
}, | |
"execution_count": 8, | |
"outputs": [ | |
{ | |
"output_type": "execute_result", | |
"data": { | |
"text/plain": [ | |
"['Cologne']" | |
] | |
}, | |
"metadata": {}, | |
"execution_count": 8 | |
} | |
] | |
} | |
] | |
} |
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