Add Colab quickstart, chat template, and release metadata

Vortex_Alpha_Colab.ipynb

@@ -0,0 +1,147 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Vortex Alpha\n",
+    "\n",
+    "A compact, experimental 174.9M-parameter language model. The final public name is still undecided.\n",
+    "\n",
+    "This notebook loads the public Hugging Face checkpoint with the standard Transformers API. Vortex is a research preview: expect factual, arithmetic, repetition, and long-context errors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Install the small runtime\n",
+    "\n",
+    "On Colab, select a GPU runtime when available. The model is small enough to fit comfortably in a typical Colab GPU, although this reference implementation does not use a KV cache."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip -q install -U \"transformers>=4.45\" sentencepiece safetensors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Load Vortex from the Hub\n",
+    "\n",
+    "`trust_remote_code=True` is required because Vortex has a custom GQA + QK-Norm implementation. The repository contains the configuration, model, tokenizer, and generation code used here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+    "\n",
+    "REPO = \"North-ML1/vortex-alpha\"\n",
+    "if torch.cuda.is_available():\n",
+    "    device = torch.device(\"cuda\")\n",
+    "    dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16\n",
+    "else:\n",
+    "    device = torch.device(\"cpu\")\n",
+    "    dtype = torch.float32\n",
+    "\n",
+    "tokenizer = AutoTokenizer.from_pretrained(REPO, trust_remote_code=True)\n",
+    "model = AutoModelForCausalLM.from_pretrained(\n",
+    "    REPO, trust_remote_code=True, torch_dtype=dtype\n",
+    ").to(device).eval()\n",
+    "\n",
+    "print(\"device:\", device)\n",
+    "print(\"dtype:\", next(model.parameters()).dtype)\n",
+    "print(\"parameters:\", model.num_parameters())\n",
+    "print(\"tokenizer vocabulary:\", tokenizer.vocab_size)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Ask a question with the built-in chat template"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ask(question: str, max_new_tokens: int = 96) -> str:\n",
+    "    messages = [{\"role\": \"user\", \"content\": question}]\n",
+    "    prompt = tokenizer.apply_chat_template(\n",
+    "        messages, tokenize=False, add_generation_prompt=True\n",
+    "    )\n",
+    "    inputs = tokenizer(prompt, return_tensors=\"pt\").to(device)\n",
+    "    with torch.inference_mode():\n",
+    "        generated = model.generate(\n",
+    "            **inputs,\n",
+    "            max_new_tokens=max_new_tokens,\n",
+    "            do_sample=False,\n",
+    "            pad_token_id=tokenizer.pad_token_id,\n",
+    "            eos_token_id=tokenizer.eos_token_id,\n",
+    "        )\n",
+    "    new_tokens = generated[0, inputs[\"input_ids\"].shape[1]:]\n",
+    "    return tokenizer.decode(new_tokens, skip_special_tokens=True)\n",
+    "\n",
+    "print(ask(\"Explain why the sky appears blue in two short paragraphs.\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "questions = [\n",
+    "    \"Solve 3x + 5 = 20 and show the steps.\",\n",
+    "    \"Write a short Python function that returns the largest number in a list.\",\n",
+    "    \"Summarize: The museum opens at 9, closes at 5, and admission is free on Sunday.\",\n",
+    "]\n",
+    "for question in questions:\n",
+    "    print(f\"\\nUSER: {question}\\nVORTEX: {ask(question)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Notes\n",
+    "\n",
+    "- `model.safetensors` is the experimental instruction/tool-format preview.\n",
+    "- `base_model.safetensors` is the corresponding pretrained base; the notebook loads the instruction preview by default.\n",
+    "- The model may emit a `CALL {json}` tool request, but this notebook does not provide external tools.\n",
+    "- Do not rely on Vortex for medical, legal, financial, or other high-stakes decisions."
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

chat_template.jinja

@@ -0,0 +1,13 @@
+{%- if messages and messages[0]['role'] != 'system' %}
+[SYSTEM]
+You are a helpful assistant. Answer clearly and say when information is missing.
+</s>
+{%- endif %}
+{%- for message in messages %}
+[{{ message['role']|upper }}]
+{{ message['content'] }}
+</s>
+{%- endfor %}
+{%- if add_generation_prompt %}
+[ASSISTANT]
+{%- endif %}

requirements.txt

@@ -0,0 +1,4 @@
+torch>=2.1
+transformers>=4.45
+sentencepiece>=0.2
+safetensors>=0.4

tokenizer_config.json

@@ -10,6 +10,7 @@
   "pad_token": "</s>",
   "model_max_length": 4096,
   "padding_side": "left",
+  "chat_template": "{%- if messages and messages[0]['role'] != 'system' %}[SYSTEM]\nYou are a helpful assistant. Answer clearly and say when information is missing.\n</s>\n{%- endif %}{%- for message in messages %}[{{ message['role']|upper }}]\n{{ message['content'] }}\n</s>\n{%- endfor %}{%- if add_generation_prompt %}[ASSISTANT]\n{%- endif %}",
   "add_bos_token": false,
   "add_eos_token": false
 }