Upload purple_squirrel_r1_demo.ipynb with huggingface_hub

purple_squirrel_r1_demo.ipynb

@@ -0,0 +1,181 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Purple Squirrel R1 - Demo Notebook\n",
+    "\n",
+    "This notebook demonstrates how to use the Purple Squirrel R1 model, a fine-tuned version of DeepSeek-R1-Distill-Llama-8B specialized for Purple Squirrel platform questions.\n",
+    "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/purplesquirrelnetworks/purple-squirrel-r1/blob/main/purple_squirrel_r1_demo.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Option 1: Use the REST API (Recommended)\n",
+    "\n",
+    "The fastest way to use the model - no GPU required!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "\n",
+    "API_URL = \"https://purplesquirrelnetworks--purple-squirrel-r1-api-purplesqu-1e3c01.modal.run\"\n",
+    "\n",
+    "def ask_purple_squirrel(prompt, max_tokens=256, temperature=0.3):\n",
+    "    response = requests.post(\n",
+    "        API_URL,\n",
+    "        json={\"prompt\": prompt, \"max_tokens\": max_tokens, \"temperature\": temperature}\n",
+    "    )\n",
+    "    return response.json()[\"response\"]\n",
+    "\n",
+    "# Test it out\n",
+    "print(ask_purple_squirrel(\"What is Purple Squirrel?\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# More examples\n",
+    "questions = [\n",
+    "    \"Can I mint NFTs with Purple Squirrel?\",\n",
+    "    \"What AI capabilities does Purple Squirrel have?\",\n",
+    "    \"How does Purple Squirrel handle video transcription?\",\n",
+    "]\n",
+    "\n",
+    "for q in questions:\n",
+    "    print(f\"Q: {q}\")\n",
+    "    print(f\"A: {ask_purple_squirrel(q)}\")\n",
+    "    print(\"-\" * 50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Option 2: Load Model Locally (Requires GPU)\n",
+    "\n",
+    "For local inference with full control. Requires ~16GB VRAM or use 4-bit quantization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install dependencies\n",
+    "!pip install -q torch transformers accelerate bitsandbytes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n",
+    "\n",
+    "MODEL_ID = \"purplesquirrelnetworks/purple-squirrel-r1\"\n",
+    "\n",
+    "# 4-bit quantization for lower memory usage\n",
+    "quantization_config = BitsAndBytesConfig(\n",
+    "    load_in_4bit=True,\n",
+    "    bnb_4bit_compute_dtype=torch.bfloat16,\n",
+    "    bnb_4bit_use_double_quant=True,\n",
+    "    bnb_4bit_quant_type=\"nf4\",\n",
+    ")\n",
+    "\n",
+    "print(\"Loading tokenizer...\")\n",
+    "tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)\n",
+    "\n",
+    "print(\"Loading model with 4-bit quantization...\")\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    MODEL_ID,\n",
+    "    quantization_config=quantization_config,\n",
+    "    device_map=\"auto\",\n",
+    ")\n",
+    "print(\"Model loaded!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def generate_response(prompt, max_tokens=256, temperature=0.3):\n",
+    "    messages = [{\"role\": \"user\", \"content\": prompt}]\n",
+    "    formatted = tokenizer.apply_chat_template(\n",
+    "        messages, tokenize=False, add_generation_prompt=True\n",
+    "    )\n",
+    "    inputs = tokenizer(formatted, return_tensors=\"pt\").to(model.device)\n",
+    "    \n",
+    "    outputs = model.generate(\n",
+    "        **inputs,\n",
+    "        max_new_tokens=max_tokens,\n",
+    "        temperature=temperature,\n",
+    "        do_sample=True,\n",
+    "        pad_token_id=tokenizer.eos_token_id,\n",
+    "        repetition_penalty=1.1,\n",
+    "    )\n",
+    "    \n",
+    "    response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
+    "    # Extract assistant response\n",
+    "    if \"<|Assistant|>\" in response:\n",
+    "        response = response.split(\"<|Assistant|>\")[-1]\n",
+    "    if \"</think>\" in response:\n",
+    "        response = response.split(\"</think>\")[-1].strip()\n",
+    "    return response\n",
+    "\n",
+    "# Test\n",
+    "print(generate_response(\"What is Purple Squirrel?\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Info\n",
+    "\n",
+    "| Attribute | Value |\n",
+    "|-----------|-------|\n",
+    "| Base Model | DeepSeek-R1-Distill-Llama-8B |\n",
+    "| Parameters | 8B |\n",
+    "| Training Method | LoRA |\n",
+    "| Training Examples | 74 |\n",
+    "| Token Accuracy | 91% |\n",
+    "\n",
+    "## Links\n",
+    "\n",
+    "- [Model on HuggingFace](https://huggingface.co/purplesquirrelnetworks/purple-squirrel-r1)\n",
+    "- [Purple Squirrel Networks](https://purplesquirrel.io)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}