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shibanovp/2023-06-04_falcon-7b-instruct_langchain.ipynb

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2023-06-04_falcon-7b-instruct_langchain.ipynb
{
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"source": [
"MIT License\n",
"\n",
"Copyright (c) 2023 Pavel Shibanov [https://blog.experienced.dev](https://blog.experienced.dev/falcon-7b-instruct-the-open-source-chatgpt-alternative-with-langchain-on-google-colab/?utm_source=github&utm_medium=notebook&utm_content=falcon-7b-instruct)\n",
"\n",
"Permission is hereby granted, free of charge, to any person obtaining a copy\n",
"of this software and associated documentation files (the \"Software\"), to deal\n",
"in the Software without restriction, including without limitation the rights\n",
"to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
"copies of the Software, and to permit persons to whom the Software is\n",
"furnished to do so, subject to the following conditions:\n",
"\n",
"The above copyright notice and this permission notice shall be included in all\n",
"copies or substantial portions of the Software.\n",
"\n",
"THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
"IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
"FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
"AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
"LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
"OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
"SOFTWARE."
]
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"id": "wd7S2pye0m9W",
"outputId": "eb5f0d2e-87ec-4c65-e8e3-b728ec6e1301"
},
"outputs": [],
"source": [
"!nvidia-smi"
]
},
{
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"outputs": [],
"source": [
"%pip install transformers accelerate einops langchain"
]
},
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"source": [
"from transformers import AutoTokenizer\n",
"\n",
"model = \"tiiuae/falcon-7b-instruct\"\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(model)"
]
},
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"outputs": [],
"source": [
"import torch\n",
"import transformers\n",
"\n",
"pipeline = transformers.pipeline(\n",
" \"text-generation\",\n",
" model=model,\n",
" tokenizer=tokenizer,\n",
" torch_dtype=torch.bfloat16,\n",
" trust_remote_code=True,\n",
" device_map=\"auto\",\n",
" max_length=200,\n",
" do_sample=True,\n",
" top_k=10,\n",
" num_return_sequences=1,\n",
" eos_token_id=tokenizer.eos_token_id,\n",
" pad_token_id=tokenizer.eos_token_id,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "i5gc676Z7ixq"
},
"outputs": [],
"source": [
"from langchain import HuggingFacePipeline\n",
"\n",
"llm = HuggingFacePipeline(pipeline=pipeline)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7PX1f-TwZS5Q"
},
"outputs": [],
"source": [
"from IPython.core.display import display, HTML\n",
"\n",
"\n",
"def display_result(question, result):\n",
" display(HTML(f\"<h3>{question}</h3><p>{result}</p>\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 135
},
"id": "Eod0edcONiBd",
"outputId": "c94d0c08-97fc-4ce7-9f06-3709af17ee61"
},
"outputs": [],
"source": [
"question = \"Write a dystopian story about a society where computers were banned.\"\n",
"result = llm(question)\n",
"display_result(question, result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CpNqWeACUZ_i"
},
"outputs": [],
"source": [
"from langchain import PromptTemplate, LLMChain\n",
"\n",
"template = \"\"\"Question: {question}\n",
"Answer: Let's think step by step.\"\"\"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
"llm_chain = LLMChain(prompt=prompt, llm=llm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "XJOLDfKKQvrl"
},
"outputs": [],
"source": [
"questions = [\n",
" \"Who proved the Efficient Market Hypothesis?\",\n",
" \"Who disproved the Efficient Market Hypothesis?\",\n",
" \"Who proved the Poincaré conjecture for n >= 5?\", # Stephen Smale\n",
" \"Who proved the Poincaré conjecture for n = 4?\", # Michael Freedman\n",
" \"Who proved the Poincaré conjecture for n = 3?\", # Grigori Perelman\n",
" \"Is the sum of all natural numbers equal to -1/12?\",\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 263
},
"id": "mswEwtgO_j2u",
"outputId": "408cccba-1550-44f7-adb7-38e3ab983731"
},
"outputs": [],
"source": [
"for question in questions:\n",
" result = llm_chain.run(question)\n",
" display_result(question, result)"
]
}
],
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"colab": {
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