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harry85
/
finetuned-TinyLLAMA-own-data-07

Text Generation
Transformers
Safetensors
English
llama
text-generation-inference
unsloth
trl
Inference Endpoints
4-bit precision
bitsandbytes
Model card Files Community
harry85 commited on
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- {
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- "nbformat": 4,
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- "nbformat_minor": 0,
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- "metadata": {
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- "colab": {
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- "provenance": [],
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- "gpuType": "T4"
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- },
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- "kernelspec": {
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- "name": "python3",
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- "display_name": "Python 3"
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- },
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- "language_info": {
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- "name": "python"
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- },
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- "accelerator": "GPU"
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- },
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- "cells": [
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- {
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- "cell_type": "code",
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- "execution_count": 1,
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- "metadata": {
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- "id": "Hyg4prvKsTfC"
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- },
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- "outputs": [],
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- "source": [
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- "#!pip install bitsandbytes\n"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "source": [
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- "pip install accelerate"
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- ],
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- "metadata": {
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- "colab": {
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- "base_uri": "https://localhost:8080/"
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- },
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- "id": "MVqLMtMt0Uhc",
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- "outputId": "0a6c320a-3dc1-451a-98f9-83a6e03c4cfb"
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- },
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- "execution_count": 3,
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- "outputs": [
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- {
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- "output_type": "stream",
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- "name": "stdout",
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- "text": [
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- "Collecting accelerate\n",
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- " Downloading accelerate-0.32.1-py3-none-any.whl (314 kB)\n",
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- "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/314.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.6/314.1 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m314.1/314.1 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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- "\u001b[?25hRequirement already satisfied: numpy<2.0.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.25.2)\n",
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- "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.3.0+cu121)\n",
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- "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.23.4)\n",
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- "Installing collected packages: accelerate\n",
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- "Successfully installed accelerate-0.32.1\n"
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- ]
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- }
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- ]
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- },
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- {
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- "cell_type": "code",
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- "source": [
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- "!pip install accelerate\n",
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- "!pip install bitsandbytes -i https://pypi.org/simple/\n"
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- ],
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- "metadata": {
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- "colab": {
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- "base_uri": "https://localhost:8080/"
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- "id": "BeHjgLP_0laH",
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- "outputId": "1eb04743-e18a-4232-c6ed-940ff55d2a67"
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- "execution_count": 2,
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- "outputs": [
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- "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.32.1)\n",
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- "Looking in indexes: https://pypi.org/simple/\n",
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- ]
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- }
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- ]
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- },
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- {
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- "cell_type": "code",
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- "source": [
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- "# Load model directly\n",
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- "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
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- "\n",
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- "#tokenizer = AutoTokenizer.from_pretrained(\"harry85/tokenizer-finetuned-TinyLLAMA\")\n",
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- "#model = AutoModelForCausalLM.from_pretrained(\"unsloth/tinyllama-bnb-4bit\")\n",
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- "# Load model directly\n",
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- "#from transformers import AutoTokenizer, AutoModelForCausalLM\n",
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- "\n",
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- "# Load model directly\n",
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- "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
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- "\n",
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- "tokenizer = AutoTokenizer.from_pretrained(\"harry85/finetuned-TinyLLAMA\")\n",
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- "model = AutoModelForCausalLM.from_pretrained(\"harry85/finetuned-TinyLLAMA\")\n",
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- "\n",
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- "\n",
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- "# Enable native 2x faster inference if supported\n",
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- "# This feature depends on the specific model and framework used; modify as needed.\n",
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- "# For example, in the case of some models, you can use model.half() to convert to FP16 for faster inference.\n",
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- "\n",
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- "# Define the Alpaca prompt\n",
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- "alpaca_prompt = \"\"\"\\\n",
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- "### Instruction:\n",
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- "{0}\n",
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- "\n",
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- "### Input:\n",
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- "\n",
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- "{1}\n",
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- "\n",
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- "### Response:\n",
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- "{2}\"\"\"\n",
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- "\n",
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- "# Prepare the input\n",
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- "inputs = tokenizer(\n",
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- " [\n",
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- " alpaca_prompt.format(\n",
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- " \"Continue the Fibonacci sequence.\", # instruction\n",
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- " \"1, 1, 2, 3, 5, 8\", # input\n",
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- " \"\" # output - leave this blank for generation!\n",
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- " )\n",
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- " ],\n",
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- " return_tensors=\"pt\"\n",
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- ").to(\"cuda\")\n",
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- "\n",
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- "# Generate the output\n",
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- "outputs = model.generate(**inputs, max_new_tokens=64, use_cache=True)\n",
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- "\n",
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- "# Decode and print the output\n",
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- "response = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n",
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- "print(response)\n"
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- ],
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- "metadata": {
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- "colab": {
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- "base_uri": "https://localhost:8080/",
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- "height": 426
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- },
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- "id": "RxlJ3o75sUrN",
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- "outputId": "df2d46f0-8851-49c7-a549-9ea4e899e8fb"
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- },
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- "execution_count": 9,
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- "outputs": [
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- {
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- "output_type": "stream",
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- "name": "stderr",
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- "text": [
241
- "Unused kwargs: ['quant_method']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>.\n",
242
- "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n"
243
- ]
244
- },
245
- {
246
- "output_type": "error",
247
- "ename": "OSError",
248
- "evalue": "harry85/finetuned-TinyLLAMA does not appear to have a file named pytorch_model.bin, model.safetensors, tf_model.h5, model.ckpt or flax_model.msgpack.",
249
- "traceback": [
250
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
251
- "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)",
252
- "\u001b[0;32m<ipython-input-9-8c32d4a0ac90>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mtokenizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAutoTokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"harry85/finetuned-TinyLLAMA\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAutoModelForCausalLM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"harry85/finetuned-TinyLLAMA\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
253
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m 561\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_mapping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 562\u001b[0m \u001b[0mmodel_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_model_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_mapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 563\u001b[0;31m return model_class.from_pretrained(\n\u001b[0m\u001b[1;32m 564\u001b[0m \u001b[0mpretrained_model_name_or_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmodel_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhub_kwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 565\u001b[0m )\n",
254
- "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/modeling_utils.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m 3449\u001b[0m )\n\u001b[1;32m 3450\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3451\u001b[0;31m raise EnvironmentError(\n\u001b[0m\u001b[1;32m 3452\u001b[0m \u001b[0;34mf\"{pretrained_model_name_or_path} does not appear to have a file named\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3453\u001b[0m \u001b[0;34mf\" {_add_variant(WEIGHTS_NAME, variant)}, {_add_variant(SAFE_WEIGHTS_NAME, variant)},\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
255
- "\u001b[0;31mOSError\u001b[0m: harry85/finetuned-TinyLLAMA does not appear to have a file named pytorch_model.bin, model.safetensors, tf_model.h5, model.ckpt or flax_model.msgpack."
256
- ]
257
- }
258
- ]
259
- },
260
- {
261
- "cell_type": "code",
262
- "source": [],
263
- "metadata": {
264
- "id": "pG2LYZowshba"
265
- },
266
- "execution_count": null,
267
- "outputs": []
268
- }
269
- ]
270
- }