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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m152.4/152.4 kB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m25.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.0/72.0 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m11.0 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[31m9.5 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[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||
"\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
|
||
"cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
|
||
"ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
|
||
"\u001b[0m"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import torch\n",
|
||
"from transformers import AutoTokenizer, AutoModelForCausalLM\n",
|
||
"from trl import setup_chat_format\n",
|
||
"\n",
|
||
"DEVICE = \"cpu\"\n",
|
||
"if torch.cuda.is_available():\n",
|
||
" DEVICE = \"cuda\"\n",
|
||
"\n",
|
||
"# The huggingface model id for Mistral-NeMo-Minitron-8B instruct model\n",
|
||
"checkpoint = \"rasyosef/Mistral-NeMo-Minitron-8B-Chat\"\n",
|
||
"\n",
|
||
"# Download and load model and tokenizer\n",
|
||
"tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
|
||
"model = AutoModelForCausalLM.from_pretrained(\n",
|
||
" checkpoint,\n",
|
||
" torch_dtype=torch.bfloat16,\n",
|
||
" device_map=\"auto\",\n",
|
||
"\n",
|
||
" )"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
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"height": 655,
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|
||
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|
||
},
|
||
"id": "ZdNx0i4DpJbY",
|
||
"outputId": "4143576d-32c0-49e7-b017-3c2202aa27e2"
|
||
},
|
||
"execution_count": 2,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:81: UserWarning: \n",
|
||
"Access to the secret `HF_TOKEN` has not been granted on this notebook.\n",
|
||
"You will not be requested again.\n",
|
||
"Please restart the session if you want to be prompted again.\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
},
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"tokenizer_config.json: 0%| | 0.00/178k [00:00<?, ?B/s]"
|
||
],
|
||
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|
||
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||
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||
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|
||
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||
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|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/plain": [
|
||
"tokenizer.json: 0%| | 0.00/9.26M [00:00<?, ?B/s]"
|
||
],
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||
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||
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||
{
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||
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||
"data": {
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||
"text/plain": [
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||
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||
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||
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"metadata": {}
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||
},
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
|
||
]
|
||
},
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||
{
|
||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
{
|
||
"output_type": "display_data",
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||
"data": {
|
||
"text/plain": [
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||
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"model_id": "f91b9654bdb949b58746cf61d48aaa2a"
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"metadata": {}
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"model_id": "2a8b281e4a764ee7ad3c14c9ee5f4f3f"
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}
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"metadata": {}
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},
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"metadata": {}
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"metadata": {}
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},
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"WARNING:accelerate.big_modeling:Some parameters are on the meta device device because they were offloaded to the cpu.\n"
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]
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}
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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": 7,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 680
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},
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"id": "GYvJS_LDXoU0",
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"outputId": "5b71594e-171b-4098-eee3-e222fbfa826d"
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},
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"outputs": [
|
||
{
|
||
"output_type": "stream",
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||
"name": "stdout",
|
||
"text": [
|
||
"Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
|
||
"\n",
|
||
"Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
|
||
"Running on public URL: https://3638cee219dc5f7066.gradio.live\n",
|
||
"\n",
|
||
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
|
||
]
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},
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{
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"output_type": "display_data",
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"text/plain": [
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"<IPython.core.display.HTML object>"
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],
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"text/html": [
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"<div><iframe src=\"https://3638cee219dc5f7066.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
|
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]
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},
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"metadata": {}
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},
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{
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"output_type": "stream",
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"name": "stdout",
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||
"text": [
|
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"Keyboard interruption in main thread... closing server.\n",
|
||
"Killing tunnel 127.0.0.1:7860 <> https://3638cee219dc5f7066.gradio.live\n"
|
||
]
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},
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{
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"output_type": "execute_result",
|
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"data": {
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"text/plain": []
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 7
|
||
}
|
||
],
|
||
"source": [
|
||
"import gradio as gr\n",
|
||
"from transformers import TextIteratorStreamer, pipeline\n",
|
||
"from threading import Thread\n",
|
||
"\n",
|
||
"# Text generation pipeline\n",
|
||
"minitron = pipeline(\n",
|
||
" \"text-generation\",\n",
|
||
" tokenizer=tokenizer,\n",
|
||
" model=model,\n",
|
||
" pad_token_id=tokenizer.eos_token_id,\n",
|
||
" eos_token_id=[tokenizer.eos_token_id],\n",
|
||
" device_map=DEVICE\n",
|
||
")\n",
|
||
"\n",
|
||
"# Function that accepts a prompt and generates text using the minitron pipeline\n",
|
||
"def generate(message, chat_history, max_new_tokens):\n",
|
||
"\n",
|
||
" history = [\n",
|
||
" {\"role\": \"system\", \"content\": \"You are Minitron, a helpful AI assistant made by Nvidia and RasYosef. User will you give you a task. Your goal is to complete the task as faithfully as you can.\"}\n",
|
||
" ]\n",
|
||
"\n",
|
||
" for sent, received in chat_history:\n",
|
||
" history.append({\"role\": \"user\", \"content\": sent})\n",
|
||
" history.append({\"role\": \"assistant\", \"content\": received})\n",
|
||
"\n",
|
||
" history.append({\"role\": \"user\", \"content\": message})\n",
|
||
" #print(history)\n",
|
||
"\n",
|
||
" if len(tokenizer.apply_chat_template(history)) > 512:\n",
|
||
" yield \"chat history is too long\"\n",
|
||
" else:\n",
|
||
" # Streamer\n",
|
||
" streamer = TextIteratorStreamer(tokenizer=tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=300.0)\n",
|
||
" thread = Thread(target=minitron, kwargs={\"text_inputs\":history, \"max_new_tokens\":max_new_tokens, \"streamer\":streamer})\n",
|
||
" thread.start()\n",
|
||
"\n",
|
||
" generated_text = \"\"\n",
|
||
" for word in streamer:\n",
|
||
" generated_text += word\n",
|
||
" response = generated_text.strip()\n",
|
||
"\n",
|
||
" yield response\n",
|
||
"\n",
|
||
"# Chat interface with gradio\n",
|
||
"with gr.Blocks() as demo:\n",
|
||
" gr.Markdown(\"\"\"\n",
|
||
" # Mistral-NeMo-Minitron-8B Chatbot Demo\n",
|
||
" This chatbot was created using a finetuned version of Nvidia's Mistral-NeMo-Minitron-8B transformer model.\n",
|
||
" \"\"\")\n",
|
||
"\n",
|
||
" tokens_slider = gr.Slider(8, 256, value=64, label=\"Maximum new tokens\", info=\"A larger `max_new_tokens` parameter value gives you longer text responses but at the cost of a slower response time.\")\n",
|
||
"\n",
|
||
" chatbot = gr.ChatInterface(\n",
|
||
" chatbot=gr.Chatbot(height=400),\n",
|
||
" fn=generate,\n",
|
||
" additional_inputs=[tokens_slider],\n",
|
||
" stop_btn=None,\n",
|
||
" examples=[\n",
|
||
" [\"Hi\"], [\"hello\"],\n",
|
||
" [\"Translate the word 'cat' to German.\"],\n",
|
||
" [\"Recommend me three animated movies.\"],\n",
|
||
" [\"Solve this quadratic equation.\\n\\nx^2 + 3x + 2 = 0\\nwhat is the value of x?\"],\n",
|
||
" [\"Implement Euclid's GCD Algorithm in python\"],\n",
|
||
" [\"Write a python program that computes the least common multiple (lcm) of two positive integers. Format the code in markdown.\"],\n",
|
||
" [\"Molly and Abigail want to attend a beauty and modeling contest. They both want to buy new pairs of shoes and dresses. Molly buys a pair of shoes which costs $40 and a dress which costs $160. How much should Abigail budget if she wants to spend half of what Molly spent on the pair of shoes and dress?\"],\n",
|
||
" ]\n",
|
||
" )\n",
|
||
"\n",
|
||
"demo.queue().launch(debug=True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [],
|
||
"metadata": {
|
||
"id": "9OrdBqHDq8hU"
|
||
},
|
||
"execution_count": 6,
|
||
"outputs": []
|
||
}
|
||
]
|
||
} |