diff --git "a/beecoder_basic_test_notebook.ipynb" "b/beecoder_basic_test_notebook.ipynb"
new file mode 100644--- /dev/null
+++ "b/beecoder_basic_test_notebook.ipynb"
@@ -0,0 +1,2832 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# beecoder - basic test notebook\n",
+        "\n",
+        "\n",
+        "- https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA-python\n",
+        "- 🐝"
+      ],
+      "metadata": {
+        "id": "5948Y879XU-V"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lIYdn1woOS1n",
+        "outputId": "9075b1fa-bc2e-4e1a-f181-d2869f99317b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m41.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m265.7/265.7 kB\u001b[0m \u001b[31m17.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m54.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.3/88.3 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m53.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
+          ]
+        }
+      ],
+      "source": [
+        "pip install transformers==4.33.3 accelerate sentencepiece fire -q"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\"\"\"\n",
+        "simple script for testing model(s) designed to generate/complete code\n",
+        "\n",
+        "See details/args with the below.\n",
+        "    python textgen_inference_code.py --help\n",
+        "\"\"\"\n",
+        "import logging\n",
+        "import random\n",
+        "import time\n",
+        "from pathlib import Path\n",
+        "\n",
+        "import fire\n",
+        "import torch\n",
+        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+        "\n",
+        "\n",
+        "\n",
+        "class Timer:\n",
+        "    \"\"\"\n",
+        "    Basic timer utility.\n",
+        "    \"\"\"\n",
+        "\n",
+        "    def __enter__(self):\n",
+        "\n",
+        "        self.start_time = time.perf_counter()\n",
+        "        return self\n",
+        "\n",
+        "    def __exit__(self, exc_type, exc_value, traceback):\n",
+        "\n",
+        "        self.end_time = time.perf_counter()\n",
+        "        self.elapsed_time = self.end_time - self.start_time\n",
+        "        print(f\"Elapsed time: {self.elapsed_time:.4f} seconds\")\n",
+        "\n",
+        "\n",
+        "def load_model(model_name, use_fast=False):\n",
+        "    \"\"\" util for loading model and tokenizer\"\"\"\n",
+        "    print(f\"Loading model: {model_name}\")\n",
+        "    tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=use_fast)\n",
+        "    model = AutoModelForCausalLM.from_pretrained(\n",
+        "        model_name, torch_dtype=\"auto\", device_map=\"auto\"\n",
+        "    )\n",
+        "    model = torch.compile(model)\n",
+        "    return tokenizer, model\n",
+        "\n",
+        "\n",
+        "def run_inference(prompt, model, tokenizer, max_new_tokens: int = 256):\n",
+        "    \"\"\"\n",
+        "    run_inference\n",
+        "\n",
+        "    Args:\n",
+        "        prompt (TYPE): Description\n",
+        "        model (TYPE): Description\n",
+        "        tokenizer (TYPE): Description\n",
+        "        max_new_tokens (int, optional): Description\n",
+        "\n",
+        "    Returns:\n",
+        "        TYPE: Description\n",
+        "    \"\"\"\n",
+        "    print(f\"Running inference with max_new_tokens={max_new_tokens} ...\")\n",
+        "    with Timer() as timer:\n",
+        "        inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
+        "        outputs = model.generate(\n",
+        "            **inputs,\n",
+        "            max_new_tokens=max_new_tokens,\n",
+        "            min_new_tokens=8,\n",
+        "            renormalize_logits=True,\n",
+        "            no_repeat_ngram_size=8,\n",
+        "            repetition_penalty=1.04,\n",
+        "            num_beams=4,\n",
+        "            early_stopping=True,\n",
+        "        )\n",
+        "    text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]\n",
+        "    print(f\"Output text:\\n\\n{text}\")\n",
+        "    return text\n",
+        "\n",
+        "\n",
+        "def main(\n",
+        "    model_name=\"BEE-spoke-data/smol_llama-101M-GQA-python\",\n",
+        "    prompt:str=None,\n",
+        "    use_fast=False,\n",
+        "    n_tokens: int = 256,\n",
+        "):\n",
+        "    \"\"\"Summary\n",
+        "\n",
+        "    Args:\n",
+        "        model_name (str, optional): Description\n",
+        "        prompt (None, optional): specify the prompt directly (default: random choice from list)\n",
+        "        n_tokens (int, optional): max new tokens to generate\n",
+        "    \"\"\"\n",
+        "    print(f\"Inference with:\\t{model_name}, max_new_tokens:{n_tokens}\")\n",
+        "\n",
+        "    if prompt is None:\n",
+        "        prompt_list = [\n",
+        "            '''\n",
+        "            def print_primes(n: int):\n",
+        "               \"\"\"\n",
+        "               Print all primes between 1 and n\n",
+        "               \"\"\"''',\n",
+        "            \"def quantum_analysis(\",\n",
+        "            \"def sanitize_filenames(target_dir:str, recursive:False, extension\",\n",
+        "        ]\n",
+        "        prompt = random.SystemRandom().choice(prompt_list)\n",
+        "\n",
+        "    print(f\"Using prompt:\\t{prompt}\")\n",
+        "\n",
+        "    tokenizer, model = load_model(model_name, use_fast=use_fast)\n",
+        "\n",
+        "    run_inference(prompt, model, tokenizer, n_tokens)\n",
+        "\n",
+        "\n",
+        "# if __name__ == \"__main__\":\n",
+        "#     fire.Fire(main)"
+      ],
+      "metadata": {
+        "id": "Qd59E_mlW67e"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "main(\n",
+        "    prompt=None, # replace with your custom prompt string\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "mToCRUd7Xk7V",
+        "outputId": "40cd0f7f-6783-4552-dea3-eab2ffd74cd8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 796,
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+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Inference with:\tBEE-spoke-data/smol_llama-101M-GQA-python, max_new_tokens:256\n",
+            "Using prompt:\tdef quantum_analysis(\n",
+            "Loading model: BEE-spoke-data/smol_llama-101M-GQA-python\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/16.3k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "d9028652e32848c48bda98d6437b5c65"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
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+          "data": {
+            "text/plain": [
+              "added_tokens.json:   0%|          | 0.00/2.28k [00:00<?, ?B/s]"
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+            ],
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+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/663 [00:00<?, ?B/s]"
+            ],
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+        {
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+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/405M [00:00<?, ?B/s]"
+            ],
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+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "generation_config.json:   0%|          | 0.00/111 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
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+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Running inference with max_new_tokens=256 ...\n",
+            "Elapsed time: 17.1484 seconds\n",
+            "Output text:\n",
+            "\n",
+            "def quantum_analysis(self):\n",
+            "    \"\"\"\n",
+            "    Computes the quantum analysis of the quantum system.\n",
+            "\n",
+            "    :return: The quantum analysis of the quantum system. \n",
+            "    : rtype : quantum_analysis.QuantumAnalysis\n",
+            "    \"\"\"\n",
+            "    return self._quantum_analysis\n",
+            "\n",
+            "  def _quantum_analysis(self):\n",
+            "    return self._quantum\n",
+            "\n",
+            "  def _quantum(self):\n",
+            "    return self\n",
+            "\n",
+            "  def _quantum2(self):\n",
+            "    return self.quantum_analysis()\n",
+            "\n",
+            "  def _quantum3(self):\n",
+            "    return self.__quantum_analysis()\n",
+            "\n",
+            "\n",
+            "  def _quantum4(self):\n",
+            "    return self + self.__quantum_analysis()\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "v_kvIyrCXGOq"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
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+      "name": "python3"
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