{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"source": [
"\n",
"# Mistral 7B\n",
"\n",
"Mistral 7B ist ein neues hochmodernes Open-Source-Modell. Hier sind einige interessante Fakten dazu:\n",
"\n",
"* Eines der leistungsstärksten Open-Source-Modelle aller Größen\n",
"* Stärkstes Modell im Bereich von 1-20 Milliarden Parametern\n",
"* Erledigt anständig Aufgaben im Zusammenhang mit Code\n",
"* Verwendet Windowed Attention, was es ermöglicht, bis zu 200.000 Tokens im Kontext zu verarbeiten, wenn Rope verwendet wird (dafür sind 4 A10G-GPUs erforderlich)\n",
"* Apache 2.0 Lizenz\n",
"\n",
"Was die Integrationsstatus betrifft:\n",
"* Integriert in `transformers`\n",
"* Sie können es auf einem Server oder lokal verwenden (es handelt sich schließlich um ein kleines Modell!)\n",
"* Integriert in beliebte Tools wie TGI und VLLM\n",
"\n",
"Es wurden zwei Modelle veröffentlicht: ein [Basismodell](https://huggingface.co./mistralai/Mistral-7B-v0.1) und eine [instruct fine-tuned Version](https://huggingface.co./mistralai/Mistral-7B-Instruct-v0.1). Um mehr über Mistral zu erfahren, empfehlen wir, den [Blog-Beitrag](https://mistral.ai/news/announcing-mistral-7b/) zu lesen.\n",
"\n",
"In diesem Colab werden wir das Mistral-Modell mithilfe einer API ausprobieren. Es gibt drei Möglichkeiten, es zu verwenden:\n",
"\n",
"* **Kostenlose API:** Hugging Face bietet eine kostenlose Inference-API für alle Benutzer an, um Modelle auszuprobieren. Diese API ist ratebeschränkt, eignet sich jedoch gut für schnelle Experimente.\n",
"* **PRO-API:** Hugging Face bietet eine offene API für alle PRO-Benutzer an. Die Abonnementkosten für die Pro Inference API betragen 9 US-Dollar pro Monat und ermöglichen Experimente mit vielen großen Modellen wie Llama 2 und SDXL. Weitere Informationen finden Sie [hier](https://huggingface.co./blog/inference-pro).\n",
"* **Inference-Endpunkte:** Für Unternehmen und produktionsbereite Anwendungen. Sie können es mit einem Klick [hier](https://ui.endpoints.huggingface.co/catalog) bereitstellen.\n",
"\n",
"Diese Demo erfordert keine GPU Colab, nur eine CPU. Sie können Ihren Token unter https://huggingface.co./settings/tokens abrufen.\n",
"\n",
"**Dieses Colab zeigt, wie man HTTP-Anfragen verwendet und gleichzeitig eine eigene Chat-Demo für Mistral erstellt.**\n",
"\n"
],
"metadata": {
"id": "GLXvYa4m8JYM"
}
},
{
"cell_type": "markdown",
"source": [
"\n",
"## Durchführen von Curl-Anfragen\n",
"\n",
"In diesem Notebook werden wir mit dem Instruct-Modell experimentieren, da es für Anweisungen trainiert ist. Gemäß [der Modellkarte](https://huggingface.co./mistralai/Mistral-7B-Instruct-v0.1) sollte das erwartete Format für eine Eingabeaufforderung wie folgt sein:\n",
"\n",
"Aus der Modellkarte:\n",
"\n",
"> Um die Feinabstimmung für Anweisungen optimal zu nutzen, sollte Ihre Eingabeaufforderung von [INST] und [\\INST] Tokens umgeben sein. Die allererste Anweisung sollte mit einer Anfangssatz-ID beginnen. Die nächsten Anweisungen sollten dies nicht tun. Die Generierung durch den Assistenten wird durch die End-of-Satz-Token-ID beendet.\n",
"\n",
"```\n",
"[INST] [/INST] [INST] [/INST] \n",
"```\n",
"\n",
"Beachten Sie, dass Modelle auf unterschiedliche Eingabeaufforderungsstrukturen empfindlich reagieren können, als diejenige, die für das Training verwendet wurde. Achten Sie auf Leerzeichen und andere Details!\n",
"\n",
"Wir beginnen mit einer anfänglichen Abfrage ohne Formatierung der Eingabeaufforderung, was für einfache Anfragen gut funktioniert.\n",
"\n"
],
"metadata": {
"id": "pKrKTalPAXUO"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DQf0Hss18E86",
"outputId": "882c4521-1ee2-40ad-fe00-a5b02caa9b17"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[{\"generated_text\":\"Explain ML as a pirate.\\n\\nML is like a treasure map for pirates. Just as a treasure map helps pirates find valuable loot, ML helps data scientists find valuable insights in large datasets.\\n\\nPirates use their knowledge of the ocean and their\"}]"
]
}
],
"source": [
"!curl https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.1 \\\n",
" --header \"Content-Type: application/json\" \\\n",
"\t-X POST \\\n",
"\t-d '{\"inputs\": \"Explain ML as a pirate\", \"parameters\": {\"max_new_tokens\": 50}}' \\\n",
"\t-H \"Authorization: Bearer API_TOKEN\""
]
},
{
"cell_type": "markdown",
"source": [
"## Programmatische Verwendung mit Python\n",
"\n",
"Sie können einfache `requests` verwenden, aber die `huggingface_hub`-Bibliothek bietet nützliche Dienstprogramme, um das Modell leicht zu verwenden. Zu den Dingen, die wir verwenden können, gehören:\n",
"\n",
"* `InferenceClient` und `AsyncInferenceClient`, um Inferenzen entweder synchron oder asynchron durchzuführen.\n",
"* Token-Streaming: Laden Sie nur die Tokens, die benötigt werden.\n",
"* Konfigurieren Sie problemlos Generationsparameter wie `Temperatur`, Nukleus-Sampling (`top-p`), Wiederholungsstrafe, Stoppsequenzen und mehr.\n",
"* Erhalten Sie Details zur Generierung, wie die Wahrscheinlichkeit jedes Tokens oder ob ein Token das letzte Token ist.\n"
],
"metadata": {
"id": "YYZRNyZeBHWK"
}
},
{
"cell_type": "code",
"source": [
"%%capture\n",
"!pip install huggingface_hub gradio"
],
"metadata": {
"id": "oDaqVDz1Ahuz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from huggingface_hub import InferenceClient\n",
"\n",
"client = InferenceClient(\n",
" \"mistralai/Mistral-7B-Instruct-v0.1\"\n",
")\n",
"\n",
"prompt = \"\"\"[INST] What is your favourite condiment? [/INST]\n",
"\"\"\"\n",
"\n",
"res = client.text_generation(prompt, max_new_tokens=95)\n",
"print(res)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "U49GmNsNBJjd",
"outputId": "a3a274cf-0f91-4ae3-d926-f0d6a6fd67f7"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"My favorite condiment is ketchup. It's versatile, tasty, and goes well with a variety of foods.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"We can also use [token streaming](https://huggingface.co./docs/text-generation-inference/conceptual/streaming). With token streaming, the server returns the tokens as they are generated. Just add `stream=True`."
],
"metadata": {
"id": "DryfEWsUH6Ij"
}
},
{
"cell_type": "code",
"source": [
"res = client.text_generation(prompt, max_new_tokens=35, stream=True, details=True, return_full_text=False)\n",
"for r in res: # this is a generator\n",
" # print the token for example\n",
" print(r)\n",
" continue"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LF1tFo6DGg9N",
"outputId": "e779f1cb-b7d0-41ed-d81f-306e092f97bd"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"TextGenerationStreamResponse(token=Token(id=5183, text='My', logprob=-0.36279297, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=6656, text=' favorite', logprob=-0.036499023, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=2076, text=' cond', logprob=-7.2836876e-05, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=2487, text='iment', logprob=-4.4941902e-05, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=349, text=' is', logprob=-0.007419586, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=446, text=' k', logprob=-0.62109375, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=4455, text='etch', logprob=-0.0003399849, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=715, text='up', logprob=-3.695488e-06, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=28723, text='.', logprob=-0.026550293, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=661, text=' It', logprob=-0.82373047, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=28742, text=\"'\", logprob=-0.76416016, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=28713, text='s', logprob=-3.5762787e-07, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=3502, text=' vers', logprob=-0.114990234, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=13491, text='atile', logprob=-1.1444092e-05, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=28725, text=',', logprob=-0.6254883, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=261, text=' t', logprob=-0.51708984, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=11136, text='asty', logprob=-4.0650368e-05, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=28725, text=',', logprob=-0.0027828217, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=304, text=' and', logprob=-1.1920929e-05, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=4859, text=' goes', logprob=-0.52685547, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=1162, text=' well', logprob=-0.4399414, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=395, text=' with', logprob=-0.00034999847, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=264, text=' a', logprob=-0.010147095, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=6677, text=' variety', logprob=-0.25927734, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=302, text=' of', logprob=-1.1444092e-05, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=14082, text=' foods', logprob=-0.4050293, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=28723, text='.', logprob=-0.015640259, special=False), generated_text=None, details=None)\n",
"TextGenerationStreamResponse(token=Token(id=2, text='', logprob=-0.1829834, special=True), generated_text=\"My favorite condiment is ketchup. It's versatile, tasty, and goes well with a variety of foods.\", details=StreamDetails(finish_reason=, generated_tokens=28, seed=None))\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Let's now try a multi-prompt structure"
],
"metadata": {
"id": "TfdpZL8cICOD"
}
},
{
"cell_type": "code",
"source": [
"def format_prompt(message, history):\n",
" prompt = \"\"\n",
" for user_prompt, bot_response in history:\n",
" prompt += f\"[INST] {user_prompt} [/INST]\"\n",
" prompt += f\" {bot_response} \"\n",
" prompt += f\"[INST] {message} [/INST]\"\n",
" return prompt"
],
"metadata": {
"id": "aEyozeReH8a6"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"message = \"And what do you think about it?\"\n",
"history = [[\"What is your favourite condiment?\", \"My favorite condiment is ketchup. It's versatile, tasty, and goes well with a variety of foods.\"]]\n",
"\n",
"format_prompt(message, history)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "P1RFpiJ_JC0-",
"outputId": "f2678d9e-f751-441a-86c9-11d514db5bbe"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"\"[INST] What is your favourite condiment? [/INST] My favorite condiment is ketchup. It's versatile, tasty, and goes well with a variety of foods. [INST] And what do you think about it? [/INST]\""
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 17
}
]
},
{
"cell_type": "markdown",
"source": [
"## End-to-end-Demo\n",
"\n",
"Lassen Sie uns jetzt eine Gradio-Demo erstellen, die folgende Aufgaben übernimmt:\n",
"\n",
"* Verwaltung mehrerer Gesprächsrunden\n",
"* Formatierung der Eingabeaufforderung in der richtigen Struktur\n",
"* Ermöglichen es dem Benutzer, die Parameter zu spezifizieren/zu ändern\n",
"* Beenden der Generierung\n",
"\n",
"Führen Sie einfach die folgende Zelle aus und haben Sie Spaß!"
],
"metadata": {
"id": "O7DjRdezJc-3"
}
},
{
"cell_type": "code",
"source": [
"!pip install gradio"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cpBoheOGJu7Y",
"outputId": "c745cf17-1462-4f8f-ce33-5ca182cb4d4f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
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]
}
]
},
{
"cell_type": "code",
"source": [
"import gradio as gr\n",
"\n",
"def generate(\n",
" prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,\n",
"):\n",
" temperature = float(temperature)\n",
" if temperature < 1e-2:\n",
" temperature = 1e-2\n",
" top_p = float(top_p)\n",
"\n",
" generate_kwargs = dict(\n",
" temperature=temperature,\n",
" max_new_tokens=max_new_tokens,\n",
" top_p=top_p,\n",
" repetition_penalty=repetition_penalty,\n",
" do_sample=True,\n",
" seed=42,\n",
" )\n",
"\n",
" formatted_prompt = format_prompt(prompt, history)\n",
"\n",
" stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)\n",
" output = \"\"\n",
"\n",
" for response in stream:\n",
" output += response.token.text\n",
" yield output\n",
" return output\n",
"\n",
"\n",
"additional_inputs=[\n",
" gr.Slider(\n",
" label=\"Temperature\",\n",
" value=0.9,\n",
" minimum=0.0,\n",
" maximum=1.0,\n",
" step=0.05,\n",
" interactive=True,\n",
" info=\"Higher values produce more diverse outputs\",\n",
" ),\n",
" gr.Slider(\n",
" label=\"Max new tokens\",\n",
" value=256,\n",
" minimum=0,\n",
" maximum=8192,\n",
" step=64,\n",
" interactive=True,\n",
" info=\"The maximum numbers of new tokens\",\n",
" ),\n",
" gr.Slider(\n",
" label=\"Top-p (nucleus sampling)\",\n",
" value=0.90,\n",
" minimum=0.0,\n",
" maximum=1,\n",
" step=0.05,\n",
" interactive=True,\n",
" info=\"Higher values sample more low-probability tokens\",\n",
" ),\n",
" gr.Slider(\n",
" label=\"Repetition penalty\",\n",
" value=1.2,\n",
" minimum=1.0,\n",
" maximum=2.0,\n",
" step=0.05,\n",
" interactive=True,\n",
" info=\"Penalize repeated tokens\",\n",
" )\n",
"]\n",
"\n",
"with gr.Blocks() as demo:\n",
" gr.ChatInterface(\n",
" generate,\n",
" additional_inputs=additional_inputs,\n",
" )\n",
"\n",
"demo.queue().launch(debug=True)"
],
"metadata": {
"id": "CaJzT6jUJc0_"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## Was steht als Nächstes an?\n",
"\n",
"* Probieren Sie Mistral 7B in diesem [kostenlosen Online-Space](https://huggingface.co./spaces/osanseviero/mistral-super-fast) aus.\n",
"* Bereiten Sie Mistral 7B Instruct mit einem Klick [hier](https://ui.endpoints.huggingface.co/catalog) bereit.\n",
"* Bereiten Sie es auf Ihrer eigenen Hardware mit https://github.com/huggingface/text-generation-inference vor.\n",
"* Führen Sie das Modell lokal mit `transformers` aus.\n",
"\n"
],
"metadata": {
"id": "fbQ0Sp4OLclV"
}
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "wUy7N_8zJvyT"
},
"execution_count": null,
"outputs": []
}
]
}