Merge pull request #778 from javieroteromarquez/jom/week3
Javi Bootcamp Exercise Week 3 and fix folder week2
This commit is contained in:
Ed Donner
GitHub
@@ -0,0 +1,573 @@
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"source": [
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"!pip install -q --upgrade bitsandbytes accelerate"
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]
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"id": "FW8nl3XRFrz0"
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},
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"outputs": [],
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"source": [
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"# imports\n",
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"\n",
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"import os\n",
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"import requests\n",
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"from IPython.display import Markdown, display, update_display\n",
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"from openai import OpenAI\n",
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"from google.colab import drive\n",
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"from huggingface_hub import login\n",
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"from google.colab import userdata\n",
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"from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, BitsAndBytesConfig\n",
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"import torch"
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]
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},
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"id": "xYW8kQYtF-3L"
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},
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"outputs": [],
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"source": [
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"hf_token = userdata.get('HF_TOKEN')\n",
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"login(hf_token, add_to_git_credential=True)\n",
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"\n",
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"DEEPSEEK = \"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B\"\n",
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"LLAMA = \"meta-llama/Llama-3.2-3B-Instruct\""
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]
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},
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"id": "piEMmcSfMH-O"
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},
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"outputs": [],
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"source": [
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"system_message = \"\"\"\n",
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"You are an specialized tutor in creating flashcards about whatever topic the user decides to research.\n",
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"They need to be brief, with a short question and a short answer in the following markdown format example\n",
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"###TEMPLATE###\n",
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"# Flashcard 1\n",
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"<details>\n",
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"<summary>What is the capital of France?</summary>\n",
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"Paris\n",
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"</details>\n",
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"\n",
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"# Flashcard 2\n",
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"\n",
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"<details>\n",
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"<summary>What is the derivative of sin(x)?</summary>\n",
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"cos(x)\n",
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"</details>\n",
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"###TEMPLATE###\n",
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"\"\"\"\n",
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"\n"
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]
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"id": "UcRKUgcxMew6"
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},
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"outputs": [],
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"source": [
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"quant_config = BitsAndBytesConfig(\n",
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" load_in_4bit=True,\n",
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" bnb_4bit_use_double_quant=True,\n",
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" bnb_4bit_compute_dtype=torch.bfloat16,\n",
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" bnb_4bit_quant_type=\"nf4\"\n",
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")"
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"outputs": [],
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"source": [
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"# Wrapping everything in a function - and adding Streaming and generation prompts\n",
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"\n",
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"def generate(model, messages, quant=True, stream = True, max_new_tokens=500):\n",
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" tokenizer = AutoTokenizer.from_pretrained(model)\n",
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" tokenizer.pad_token = tokenizer.eos_token\n",
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" input_ids = tokenizer.apply_chat_template(messages, return_tensors=\"pt\", add_generation_prompt=True).to(\"cuda\")\n",
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" attention_mask = torch.ones_like(input_ids, dtype=torch.long, device=\"cuda\")\n",
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" streamer = TextStreamer(tokenizer)\n",
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" if quant:\n",
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" model = AutoModelForCausalLM.from_pretrained(model, quantization_config=quant_config).to(\"cuda\")\n",
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" else:\n",
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" model = AutoModelForCausalLM.from_pretrained(model).to(\"cuda\")\n",
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" if stream:\n",
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" outputs = model.generate(input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=max_new_tokens, streamer=streamer)\n",
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" else:\n",
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" outputs = model.generate(input_ids=input_ids, attention_mask=attention_mask, max_new_tokens=max_new_tokens,)\n",
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"\n",
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" response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
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" return response\n"
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]
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"import gradio as gr\n",
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"import re\n",
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"\n",
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"def call_generate(model_name, topic, num_flashcards):\n",
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" if model_name == \"LLAMA\":\n",
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" model = LLAMA\n",
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" elif model_name == \"DEEPSEEK\":\n",
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" model = DEEPSEEK\n",
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" else:\n",
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" return \"Invalid model selected.\"\n",
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"\n",
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" messages = [\n",
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" {\"role\": \"system\", \"content\": system_message},\n",
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" {\"role\": \"user\", \"content\": f\"I want to know more about {topic}. Please provide {num_flashcards} flashcards.\"}\n",
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" ]\n",
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"\n",
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" # Call your existing generate function\n",
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" response = generate(model, messages, stream=False, max_new_tokens=2000)\n",
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" text = re.sub(r'###TEMPLATE.*?###TEMPLATE', '', response, flags=re.DOTALL)\n",
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"\n",
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" result = re.search(r\"(# Flashcard 1[\\s\\S]*</details>)\", text)\n",
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"\n",
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" if result:\n",
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" response = result.group(1)\n",
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" else:\n",
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" response\n",
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" return response\n",
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"\n",
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"with gr.Blocks() as ui:\n",
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" with gr.Row():\n",
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" model_dropdown = gr.Dropdown(choices=[\"LLAMA\", \"DEEPSEEK\"], value=\"LLAMA\", label=\"Model\")\n",
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" with gr.Row():\n",
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" topic_selector = gr.Textbox(label=\"Type the topic you want flashcards:\", max_lines=1, max_length=50)\n",
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" num_flashcards = gr.Slider(\n",
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" minimum=1,\n",
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" maximum=10,\n",
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" step=1,\n",
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" value=5,\n",
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" label=\"Nr. Flashcards\",\n",
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" )\n",
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" with gr.Row():\n",
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" generate_button = gr.Button(\"Generate Flashcards\")\n",
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" with gr.Row():\n",
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" output = gr.Markdown()\n",
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"\n",
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" # Hooking up events to callbacks\n",
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" generate_button.click(\n",
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" call_generate,\n",
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" inputs=[model_dropdown, topic_selector, num_flashcards],\n",
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" outputs=output\n",
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" )\n",
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"\n",
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"ui.launch(inbrowser=True, debug=True)"
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]
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Reference in New Issue
Block a user