0bc8951...main · rowtype-yoga/compiler-compendium

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fine_tune_code_llama.ipynbnew file+7005

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1+{

2+ "cells": [

3+ {

4+ "cell_type": "markdown",

5+ "metadata": {

6+ "id": "_5rfduRD1CSF"

7+ },

8+ "source": [

9+ "# A guide to fine-tuning Code Llama\n",

10+ "\n",

11+ "**In this guide I show you how to fine-tune Code Llama to become a beast of an SQL developer. For coding tasks, you can generally get much better performance out of Code Llama than Llama 2, especially when you specialise the model on a particular task:**\n",

12+ "\n",

13+ "- I use the [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context) which is a bunch of text queries and their corresponding SQL queries\n",

14+ "- A Lora approach, quantizing the base model to int 8, freezing its weights and only training an adapter\n",

15+ "- Much of the code is borrowed from [alpaca-lora](https://github.com/tloen/alpaca-lora), but I refactored it quite a bit for this\n"

16+ ]

17+ },

18+ {

19+ "cell_type": "markdown",

20+ "metadata": {

21+ "id": "HFQ9XeBI1CSG"

22+ },

23+ "source": [

24+ "### 2. Pip installs\n"

25+ ]

26+ },

27+ {

28+ "cell_type": "code",

29+ "execution_count": null,

30+ "metadata": {

31+ "colab": {

32+ "base_uri": "https://localhost:8080/"

33+ },

34+ "id": "Y0x4OWfq1CSG",

35+ "outputId": "85954959-c527-4e20-f91b-1725a596ba31"

36+ },

37+ "outputs": [

38+ {

39+ "output_type": "stream",

40+ "name": "stdout",

41+ "text": [

42+ "Collecting git+https://github.com/huggingface/transformers.git@main\n",

43+ " Cloning https://github.com/huggingface/transformers.git (to revision main) to /tmp/pip-req-build-dsrdwty_\n",

44+ " Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-req-build-dsrdwty_\n",

45+ " Resolved https://github.com/huggingface/transformers.git to commit e65502951593a76844e872fee9c56b805598538a\n",

46+ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",

47+ " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",

48+ " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",

49+ "Collecting bitsandbytes\n",

50+ " Downloading bitsandbytes-0.43.1-py3-none-manylinux_2_24_x86_64.whl (119.8 MB)\n",

51+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",

52+ "\u001b[?25hCollecting accelerate==0.20.3\n",

53+ " Downloading accelerate-0.20.3-py3-none-any.whl (227 kB)\n",

54+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.6/227.6 kB\u001b[0m \u001b[31m31.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",

55+ "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate==0.20.3) (1.25.2)\n",

56+ "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate==0.20.3) (24.1)\n",

57+ "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate==0.20.3) (5.9.5)\n",

58+ "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate==0.20.3) (6.0.1)\n",

59+ "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from accelerate==0.20.3) (2.3.0+cu121)\n",

60+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.43.0.dev0) (3.15.3)\n",

61+ "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers==4.43.0.dev0) (0.23.4)\n",

62+ "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.43.0.dev0) (2024.5.15)\n",

63+ "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.43.0.dev0) (2.31.0)\n",

64+ "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers==4.43.0.dev0) (0.19.1)\n",

65+ "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.43.0.dev0) (0.4.3)\n",

66+ "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers==4.43.0.dev0) (4.66.4)\n",

67+ "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers==4.43.0.dev0) (2023.6.0)\n",

68+ "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers==4.43.0.dev0) (4.12.2)\n",

69+ "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->accelerate==0.20.3) (1.12.1)\n",

70+ "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->accelerate==0.20.3) (3.3)\n",

71+ "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->accelerate==0.20.3) (3.1.4)\n",

72+ "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.6.0->accelerate==0.20.3)\n",

73+ " Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",

74+ "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.6.0->accelerate==0.20.3)\n",

75+ " Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",

76+ "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.6.0->accelerate==0.20.3)\n",

77+ " Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",

78+ "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.6.0->accelerate==0.20.3)\n",

79+ " Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",

80+ "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.6.0->accelerate==0.20.3)\n",

81+ " Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",

82+ "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.6.0->accelerate==0.20.3)\n",

83+ " Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",

84+ "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.6.0->accelerate==0.20.3)\n",

85+ " Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",

86+ "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.6.0->accelerate==0.20.3)\n",

87+ " Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",

88+ "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.6.0->accelerate==0.20.3)\n",

89+ " Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",

90+ "Collecting nvidia-nccl-cu12==2.20.5 (from torch>=1.6.0->accelerate==0.20.3)\n",

91+ " Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",

92+ "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.6.0->accelerate==0.20.3)\n",

93+ " Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",

94+ "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->accelerate==0.20.3) (2.3.0)\n",

95+ "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.6.0->accelerate==0.20.3)\n",

96+ " Downloading nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",

97+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m70.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",

98+ "\u001b[?25hRequirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.43.0.dev0) (3.3.2)\n",

99+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.43.0.dev0) (3.7)\n",

100+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.43.0.dev0) (2.0.7)\n",

101+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.43.0.dev0) (2024.6.2)\n",

102+ "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.6.0->accelerate==0.20.3) (2.1.5)\n",

103+ "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.6.0->accelerate==0.20.3) (1.3.0)\n",

104+ "Building wheels for collected packages: transformers\n",

105+ " Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",

106+ " Created wheel for transformers: filename=transformers-4.43.0.dev0-py3-none-any.whl size=9337412 sha256=3bcc3ee905c1d3dd2612cb8638ad01064e2e4ea5d9f9be0743ff0dc7a392360e\n",

107+ " Stored in directory: /tmp/pip-ephem-wheel-cache-s6y6llys/wheels/cf/59/82/6492402e887a68975030bf8c06532260abc16abb7ccd8127cc\n",

108+ "Successfully built transformers\n",

109+ "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, transformers, bitsandbytes, accelerate\n",

110+ " Attempting uninstall: transformers\n",

111+ " Found existing installation: transformers 4.41.2\n",

112+ " Uninstalling transformers-4.41.2:\n",

113+ " Successfully uninstalled transformers-4.41.2\n",

114+ "Successfully installed accelerate-0.20.3 bitsandbytes-0.43.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 transformers-4.43.0.dev0\n",

115+ "Collecting git+https://github.com/huggingface/peft.git@e536616888d51b453ed354a6f1e243fecb02ea08\n",

116+ " Cloning https://github.com/huggingface/peft.git (to revision e536616888d51b453ed354a6f1e243fecb02ea08) to /tmp/pip-req-build-gxd2ld0j\n",

117+ " Running command git clone --filter=blob:none --quiet https://github.com/huggingface/peft.git /tmp/pip-req-build-gxd2ld0j\n",

118+ " Running command git rev-parse -q --verify 'sha^e536616888d51b453ed354a6f1e243fecb02ea08'\n",

119+ " Running command git fetch -q https://github.com/huggingface/peft.git e536616888d51b453ed354a6f1e243fecb02ea08\n",

120+ " Running command git checkout -q e536616888d51b453ed354a6f1e243fecb02ea08\n",

121+ " Resolved https://github.com/huggingface/peft.git to commit e536616888d51b453ed354a6f1e243fecb02ea08\n",

122+ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",

123+ " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",

124+ " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",

125+ "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from peft==0.3.0.dev0) (1.25.2)\n",

126+ "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from peft==0.3.0.dev0) (24.1)\n",

127+ "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from peft==0.3.0.dev0) (5.9.5)\n",

128+ "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from peft==0.3.0.dev0) (6.0.1)\n",

129+ "Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from peft==0.3.0.dev0) (2.3.0+cu121)\n",

130+ "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from peft==0.3.0.dev0) (4.43.0.dev0)\n",

131+ "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (from peft==0.3.0.dev0) (0.20.3)\n",

132+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (3.15.3)\n",

133+ "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (4.12.2)\n",

134+ "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (1.12.1)\n",

135+ "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (3.3)\n",

136+ "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (3.1.4)\n",

137+ "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (2023.6.0)\n",

138+ "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (12.1.105)\n",

139+ "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (12.1.105)\n",

140+ "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (12.1.105)\n",

141+ "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (8.9.2.26)\n",

142+ "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (12.1.3.1)\n",

143+ "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (11.0.2.54)\n",

144+ "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (10.3.2.106)\n",

145+ "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (11.4.5.107)\n",

146+ "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (12.1.0.106)\n",

147+ "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (2.20.5)\n",

148+ "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (12.1.105)\n",

149+ "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (2.3.0)\n",

150+ "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.13.0->peft==0.3.0.dev0) (12.5.40)\n",

151+ "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers->peft==0.3.0.dev0) (0.23.4)\n",

152+ "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers->peft==0.3.0.dev0) (2024.5.15)\n",

153+ "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers->peft==0.3.0.dev0) (2.31.0)\n",

154+ "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers->peft==0.3.0.dev0) (0.19.1)\n",

155+ "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers->peft==0.3.0.dev0) (0.4.3)\n",

156+ "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers->peft==0.3.0.dev0) (4.66.4)\n",

157+ "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13.0->peft==0.3.0.dev0) (2.1.5)\n",

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159+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft==0.3.0.dev0) (3.7)\n",

160+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft==0.3.0.dev0) (2.0.7)\n",

161+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft==0.3.0.dev0) (2024.6.2)\n",

162+ "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13.0->peft==0.3.0.dev0) (1.3.0)\n",

163+ "Building wheels for collected packages: peft\n",

164+ " Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",

165+ " Created wheel for peft: filename=peft-0.3.0.dev0-py3-none-any.whl size=41637 sha256=8dca0349c03a339974d6963d5025cc47db6449bc9faeeafa7d1a7e9e811924fd\n",

166+ " Stored in directory: /root/.cache/pip/wheels/02/0a/9a/b9755f6b184f58a5a44baf58dd9664a49b3295e4b3e9a1f174\n",

167+ "Successfully built peft\n",

168+ "Installing collected packages: peft\n",

169+ "Successfully installed peft-0.3.0.dev0\n",

170+ "Collecting datasets==2.10.1\n",

171+ " Downloading datasets-2.10.1-py3-none-any.whl (469 kB)\n",

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206+ "INFO: pip is looking at multiple versions of multiprocess to determine which version is compatible with other requirements. This could take a while.\n",

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216+ "Installing collected packages: xxhash, dill, responses, multiprocess, datasets\n",

217+ "Successfully installed datasets-2.10.1 dill-0.3.6 multiprocess-0.70.14 responses-0.18.0 xxhash-3.4.1\n",

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246+ "Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->gitpython!=3.1.29,>=1.0.0->wandb)\n",

247+ " Downloading smmap-5.0.1-py3-none-any.whl (24 kB)\n",

248+ "Installing collected packages: smmap, setproctitle, sentry-sdk, docker-pycreds, gitdb, gitpython, wandb\n",

249+ "Successfully installed docker-pycreds-0.4.0 gitdb-4.0.11 gitpython-3.1.43 sentry-sdk-2.7.1 setproctitle-1.3.3 smmap-5.0.1 wandb-0.17.3\n",

250+ "Collecting git+https://github.com/huggingface/accelerate\n",

251+ " Cloning https://github.com/huggingface/accelerate to /tmp/pip-req-build-ybbq40u5\n",

252+ " Running command git clone --filter=blob:none --quiet https://github.com/huggingface/accelerate /tmp/pip-req-build-ybbq40u5\n",

253+ " Resolved https://github.com/huggingface/accelerate to commit c799c198e9d8096e3fffeb962b3adf34e5397d5e\n",

254+ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",

255+ " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",

256+ " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",

257+ "Requirement already satisfied: numpy<2.0.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate==0.32.0.dev0) (1.25.2)\n",

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261+ "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate==0.32.0.dev0) (2.3.0+cu121)\n",

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267+ "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (3.3)\n",

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270+ "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (12.1.105)\n",

271+ "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (12.1.105)\n",

272+ "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (12.1.105)\n",

273+ "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (8.9.2.26)\n",

274+ "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (12.1.3.1)\n",

275+ "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (11.0.2.54)\n",

276+ "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (10.3.2.106)\n",

277+ "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (11.4.5.107)\n",

278+ "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (12.1.0.106)\n",

279+ "Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (2.20.5)\n",

280+ "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (12.1.105)\n",

281+ "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.32.0.dev0) (2.3.0)\n",

282+ "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate==0.32.0.dev0) (12.5.40)\n",

283+ "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate==0.32.0.dev0) (2.31.0)\n",

284+ "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate==0.32.0.dev0) (4.66.4)\n",

285+ "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate==0.32.0.dev0) (2.1.5)\n",

286+ "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate==0.32.0.dev0) (3.3.2)\n",

287+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate==0.32.0.dev0) (3.7)\n",

288+ "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate==0.32.0.dev0) (2.0.7)\n",

289+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate==0.32.0.dev0) (2024.6.2)\n",

290+ "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate==0.32.0.dev0) (1.3.0)\n",

291+ "Building wheels for collected packages: accelerate\n",

292+ " Building wheel for accelerate (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",

293+ " Created wheel for accelerate: filename=accelerate-0.32.0.dev0-py3-none-any.whl size=311915 sha256=d60c1b8c937d4c9992b76184d33f8626990edd6ae910b2025ddf25e180cf6fb0\n",

294+ " Stored in directory: /tmp/pip-ephem-wheel-cache-4f_elh6m/wheels/f6/c7/9d/1b8a5ca8353d9307733bc719107acb67acdc95063bba749f26\n",

295+ "Successfully built accelerate\n",

296+ "Installing collected packages: accelerate\n",

297+ " Attempting uninstall: accelerate\n",

298+ " Found existing installation: accelerate 0.20.3\n",

299+ " Uninstalling accelerate-0.20.3:\n",

300+ " Successfully uninstalled accelerate-0.20.3\n",

301+ "Successfully installed accelerate-0.32.0.dev0\n"

302+ ]

303+ }

304+ ],

305+ "source": [

306+ "!pip install git+https://github.com/huggingface/transformers.git@main bitsandbytes accelerate==0.20.3 # we need latest transformers for this\n",

307+ "!pip install git+https://github.com/huggingface/peft.git@e536616888d51b453ed354a6f1e243fecb02ea08\n",

308+ "!pip install datasets==2.10.1\n",

309+ "!pip install wandb\n",

310+ "!pip install git+https://github.com/huggingface/accelerate"

311+ ]

312+ },

313+ {

314+ "cell_type": "markdown",

315+ "metadata": {

316+ "id": "mCmGzYg51CSH"

317+ },

318+ "source": [

319+ "I used an A100 GPU machine with Python 3.10 and cuda 11.8 to run this notebook. It took about an hour to run."

320+ ]

321+ },

322+ {

323+ "cell_type": "markdown",

324+ "metadata": {

325+ "id": "0mu9JczX1CSH"

326+ },

327+ "source": [

328+ "### Loading libraries\n"

329+ ]

330+ },

331+ {

332+ "cell_type": "code",

333+ "source": [

334+ "#import locale # colab workaround (MS - why?)\n",

335+ "#locale.getpreferredencoding = lambda _: \"UTF-8\" # colab workaround (MS - why?)"

336+ ],

337+ "metadata": {

338+ "id": "mploAwenyY1b"

339+ },

340+ "execution_count": null,

341+ "outputs": []

342+ },

343+ {

344+ "cell_type": "code",

345+ "execution_count": null,

346+ "metadata": {

347+ "id": "oTeYW8z51CSH"

348+ },

349+ "outputs": [],

350+ "source": [

351+ "from datetime import datetime\n",

352+ "import os\n",

353+ "import sys\n",

354+ "\n",

355+ "import torch\n",

356+ "from peft import (\n",

357+ " LoraConfig,\n",

358+ " get_peft_model,\n",

359+ " get_peft_model_state_dict,\n",

360+ " prepare_model_for_int8_training,\n",

361+ " set_peft_model_state_dict,\n",

362+ ")\n",

363+ "from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForSeq2Seq\n"

364+ ]

365+ },

366+ {

367+ "cell_type": "markdown",

368+ "metadata": {

369+ "id": "32zH9-hM1CSH"

370+ },

371+ "source": [

372+ "(If you have import errors, try restarting your Jupyter kernel)\n"

373+ ]

374+ },

375+ {

376+ "cell_type": "markdown",

377+ "metadata": {

378+ "id": "4M9KyT0S1CSH"

379+ },

380+ "source": [

381+ "### Load dataset\n"

382+ ]

383+ },

384+ {

385+ "cell_type": "code",

386+ "execution_count": null,

387+ "metadata": {

388+ "colab": {

389+ "base_uri": "https://localhost:8080/",

390+ "height": 308,

391+ "referenced_widgets": [

392+ "c31be5d602e64c5cac38693d601acff7",

393+ "0ec9f66617644ef98e22bd8eebaa1b19",

394+ "00dcb6577b0e40c2bbfdea12c0004e94",

395+ "3542e0acaa6a4adb8dbae233d0b36870",

396+ "371469506e5243ac967085e68af8a82e",

397+ "72df7d9380fd4c14b66d4c13e6c57a20",

398+ "4a7fd75012f5444b98cadecded25ed70",

399+ "3761062b59b04588bdb4ee4c8fe04ba7",

400+ "bbca0c1dae6e4cf984392075200a34d3",

401+ "3dc10ba82f8948ea92144f4a54f418f4",

402+ "c84c0cd440c84ec98f773a55180fdab2",

403+ "8edafecdeefd41329f9642c600ff7428",

404+ "8bb3a968a4b84f3c86328ce49395f90a",

405+ "cdef8836c4284d1b98dbb07e65c24ec3",

406+ "1c0077241deb4e77b8b152d6a2e2bb4c",

407+ "9e410cb390494c92b883ed7a2cc12adf",

408+ "5033c87a47504cc7b3f37257f077e021",

409+ "691a710a120b411f8ba2fa39046989ae",

410+ "e68a1ae39fe044028006a10cb74918db",

411+ "c26c9b5ac0194106af9bad10acb141b9",

412+ "4ddaf4df640548e0ab6a92281e7639ab",

413+ "17e20ebd1a674149bc2b636434971886",

414+ "a17c1fab4d444206ac36ee0ab34c68a7",

415+ "9ecd37ddb6f04483a7298015d840cd73",

416+ "298835f687b4471486d11711b857876c",

417+ "bfe35c4fef9442a5a3f788d930376ea6",

418+ "140a95c743e9405da23de4ca012d647b",

419+ "560485aef18d418ebafe2f4043762535",

420+ "2b5ef4d827a5430691d98c035bae4596",

421+ "d21b0bc311364ccea13650e956b75295",

422+ "0e483d8b7a064f919090d89248f21d76",

423+ "128472f2459a4d6eb4aa920bd78f4abc",

424+ "8c47ee45130a454f90e2d0d59b07acfc",

425+ "a8f0bd6483694aefac9a18e99f839ce2",

426+ "84ea78fb849f4558b272df0f2693bb0e",

427+ "ad15d91e8e884020a18d023696ed0897",

428+ "3937159009bc4c7aad2a7551a35a591d",

429+ "5391aa65bc764781974410753d20aeff",

430+ "93c54f0a951942cbaf0e02bf69fbf9af",

431+ "31a0b03f535a4744911f21f1e7cfd703",

432+ "3721753463e546049adec3206c344940",

433+ "968b324b55f54a4faebce33d370cf767",

434+ "ef37b621119f4cf7b7e940ba50ef970b",

435+ "9e2a30524b354887b6d3a6fe1cbdd618",

436+ "3d29b558467f4ba78fd0e7f5bc32ad71",

437+ "bdf0beca44944d078b0d86ddb4b90a34",

438+ "7f31603cfba34008a151574f054d2c27",

439+ "4047ec990aef4ea585728bb00590b665",

440+ "71720a930cb64fdb86fc7f675d7fe86e",

441+ "a6046df162534dd3bda90a2985346925",

442+ "e97366424f8e42d6acb597f3e09582a1",

443+ "113f6b41b96e419c842f499b797d7790",

444+ "4e46bba497f444a7bfd799b6cd2b9c33",

445+ "6b2fbf4b32624b6cbbd5b5d033a92b04",

446+ "64b21f0a68fc43a6aee4e7905e488a0a"

447+ ]

448+ },

449+ "id": "w44O1EK-1CSH",

450+ "outputId": "a3196ea0-b919-4717-f364-ead1ee039eb3"

451+ },

452+ "outputs": [

453+ {

454+ "output_type": "stream",

455+ "name": "stderr",

456+ "text": [

457+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",

458+ "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",

459+ "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",

460+ "You will be able to reuse this secret in all of your notebooks.\n",

461+ "Please note that authentication is recommended but still optional to access public models or datasets.\n",

462+ " warnings.warn(\n"

463+ ]

464+ },

465+ {

466+ "output_type": "display_data",

467+ "data": {

468+ "text/plain": [

469+ "Downloading readme: 0%| | 0.00/4.43k [00:00<?, ?B/s]"

470+ ],

471+ "application/vnd.jupyter.widget-view+json": {

472+ "version_major": 2,

473+ "version_minor": 0,

474+ "model_id": "c31be5d602e64c5cac38693d601acff7"

475+ }

476+ },

477+ "metadata": {}

478+ },

479+ {

480+ "output_type": "stream",

481+ "name": "stdout",

482+ "text": [

483+ "Downloading and preparing dataset json/b-mc2--sql-create-context to /root/.cache/huggingface/datasets/b-mc2___json/b-mc2--sql-create-context-b6e5c5a03fb67709/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...\n"

484+ ]

485+ },

486+ {

487+ "output_type": "display_data",

488+ "data": {

489+ "text/plain": [

490+ "Downloading data files: 0%| | 0/1 [00:00<?, ?it/s]"

491+ ],

492+ "application/vnd.jupyter.widget-view+json": {

493+ "version_major": 2,

494+ "version_minor": 0,

495+ "model_id": "8edafecdeefd41329f9642c600ff7428"

496+ }

497+ },

498+ "metadata": {}

499+ },

500+ {

501+ "output_type": "display_data",

502+ "data": {

503+ "text/plain": [

504+ "Downloading data: 0%| | 0.00/21.8M [00:00<?, ?B/s]"

505+ ],

506+ "application/vnd.jupyter.widget-view+json": {

507+ "version_major": 2,

508+ "version_minor": 0,

509+ "model_id": "a17c1fab4d444206ac36ee0ab34c68a7"

510+ }

511+ },

512+ "metadata": {}

513+ },

514+ {

515+ "output_type": "display_data",

516+ "data": {

517+ "text/plain": [

518+ "Extracting data files: 0%| | 0/1 [00:00<?, ?it/s]"

519+ ],

520+ "application/vnd.jupyter.widget-view+json": {

521+ "version_major": 2,

522+ "version_minor": 0,

523+ "model_id": "a8f0bd6483694aefac9a18e99f839ce2"

524+ }

525+ },

526+ "metadata": {}

527+ },

528+ {

529+ "output_type": "display_data",

530+ "data": {

531+ "text/plain": [

532+ "Generating train split: 0 examples [00:00, ? examples/s]"

533+ ],

534+ "application/vnd.jupyter.widget-view+json": {

535+ "version_major": 2,

536+ "version_minor": 0,

537+ "model_id": "3d29b558467f4ba78fd0e7f5bc32ad71"

538+ }

539+ },

540+ "metadata": {}

541+ },

542+ {

543+ "output_type": "stream",

544+ "name": "stdout",

545+ "text": [

546+ "Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/b-mc2___json/b-mc2--sql-create-context-b6e5c5a03fb67709/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51. Subsequent calls will reuse this data.\n"

547+ ]

548+ }

549+ ],

550+ "source": [

551+ "from datasets import load_dataset\n",

552+ "dataset = load_dataset(\"b-mc2/sql-create-context\", split=\"train\")\n",

553+ "train_dataset = dataset.train_test_split(test_size=0.1)[\"train\"]\n",

554+ "eval_dataset = dataset.train_test_split(test_size=0.1)[\"test\"]"

555+ ]

556+ },

557+ {

558+ "cell_type": "markdown",

559+ "metadata": {

560+ "id": "fJ54EffO1CSI"

561+ },

562+ "source": [

563+ "The above pulls the dataset from the Huggingface Hub and splits 10% of it into an evaluation set to check how well the model is doing through training. If you want to load your own dataset do this:\n",

564+ "\n",

565+ "```\n",

566+ "train_dataset = load_dataset('json', data_files='train_set.jsonl', split='train')\n",

567+ "eval_dataset = load_dataset('json', data_files='validation_set.jsonl', split='train')\n",

568+ "```\n",

569+ "\n",

570+ "And if you want to view any samples in the dataset just do something like:``` ```\n"

571+ ]

572+ },

573+ {

574+ "cell_type": "code",

575+ "execution_count": null,

576+ "metadata": {

577+ "colab": {

578+ "base_uri": "https://localhost:8080/"

579+ },

580+ "id": "fFbeaZzf1CSJ",

581+ "outputId": "25fc024b-2eed-4943-86c0-48542e878254"

582+ },

583+ "outputs": [

584+ {

585+ "output_type": "stream",

586+ "name": "stdout",

587+ "text": [

588+ "{'question': 'What region has giuseppe sivori as a home venue?', 'context': 'CREATE TABLE table_name_52 (region VARCHAR, home_venue VARCHAR)', 'answer': 'SELECT region FROM table_name_52 WHERE home_venue = \"giuseppe sivori\"'}\n"

589+ ]

590+ }

591+ ],

592+ "source": [

593+ "print(train_dataset[3])"

594+ ]

595+ },

596+ {

597+ "cell_type": "markdown",

598+ "metadata": {

599+ "id": "VHdMYcu61CSJ"

600+ },

601+ "source": [

602+ "Each entry is made up of a text 'question', the sql table 'context' and the 'answer'."

603+ ]

604+ },

605+ {

606+ "cell_type": "markdown",

607+ "metadata": {

608+ "id": "6ig7NvWN1CSJ"

609+ },

610+ "source": [

611+ "### Load model\n",

612+ "I load code llama from huggingface in int8. Standard for Lora:"

613+ ]

614+ },

615+ {

616+ "cell_type": "code",

617+ "execution_count": null,

618+ "metadata": {

619+ "colab": {

620+ "base_uri": "https://localhost:8080/",

621+ "height": 407,

622+ "referenced_widgets": [

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637+ "ddfbc72fff4e469d9b13755b674c0671",

638+ "d47c7f6784e44beeb1f5a51bbcae9cdd",

639+ "240dab6a9f204f9baabc4ede372793ac",

640+ "b3ac3904d16c4424a9245687228ca25d",

641+ "cf57e9a838264fd389167363af4340d2",

642+ "f71f1dd9293345d2a4aab0c817933a59",

643+ "a5dea14c16c04f60adf7c809b8535e85",

644+ "c3e1b53349a14956b4a5db17b67696cd",

645+ "7cf0c39f0a844b91be86a732a025e50d",

646+ "fb75b1fa221b4d5095e245687e4296af",

647+ "843ccc4061f7447eb86d2d6523f65ffc",

648+ "c2e16091afbb45f19346b397ced775d2",

649+ "ef6236de50fb4bf4928abb811d2f6f5c",

650+ "7e7b4d11f11a4c0886fb978ff32a68ea",

651+ "c7ef9de8b1684d98a04ae2df3a378d11",

652+ "1e862f7e12ef49448f3902d256109f6a",

653+ "da1ebe03b4f1478ebf9651b40c99b30a",

654+ "be1ebfd7cc274dd18ce5f9f157fb497d",

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728+ "e215d7711dae486189d4df6a1cd63fe4",

729+ "3019460b4af2486496430cf38447b3c0",

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744+ ]

745+ },

746+ "id": "rMnU93bY1CSJ",

747+ "outputId": "384bbb16-f429-4885-dca4-77b0f0e38355"

748+ },

749+ "outputs": [

750+ {

751+ "output_type": "display_data",

752+ "data": {

753+ "text/plain": [

754+ "config.json: 0%| | 0.00/637 [00:00<?, ?B/s]"

755+ ],

756+ "application/vnd.jupyter.widget-view+json": {

757+ "version_major": 2,

758+ "version_minor": 0,

759+ "model_id": "eab2a932d0794c46aaf4f94a9822d5b9"

760+ }

761+ },

762+ "metadata": {}

763+ },

764+ {

765+ "output_type": "stream",

766+ "name": "stderr",

767+ "text": [

768+ "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"

769+ ]

770+ },

771+ {

772+ "output_type": "display_data",

773+ "data": {

774+ "text/plain": [

775+ "model.safetensors.index.json: 0%| | 0.00/25.1k [00:00<?, ?B/s]"

776+ ],

777+ "application/vnd.jupyter.widget-view+json": {

778+ "version_major": 2,

779+ "version_minor": 0,

780+ "model_id": "bf4963f7d1d745a99869377020edd145"

781+ }

782+ },

783+ "metadata": {}

784+ },

785+ {

786+ "output_type": "display_data",

787+ "data": {

788+ "text/plain": [

789+ "Downloading shards: 0%| | 0/2 [00:00<?, ?it/s]"

790+ ],

791+ "application/vnd.jupyter.widget-view+json": {

792+ "version_major": 2,

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796+ },

797+ "metadata": {}

798+ },

799+ {

800+ "output_type": "display_data",

801+ "data": {

802+ "text/plain": [

803+ "model-00001-of-00002.safetensors: 0%| | 0.00/9.98G [00:00<?, ?B/s]"

804+ ],

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825+ "metadata": {}

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827+ {

828+ "output_type": "display_data",

829+ "data": {

830+ "text/plain": [

831+ "Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"

832+ ],

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838+ },

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840+ },

841+ {

842+ "output_type": "display_data",

843+ "data": {

844+ "text/plain": [

845+ "generation_config.json: 0%| | 0.00/116 [00:00<?, ?B/s]"

846+ ],

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852+ },

853+ "metadata": {}

854+ },

855+ {

856+ "output_type": "display_data",

857+ "data": {

858+ "text/plain": [

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860+ ],

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865+ }

866+ },

867+ "metadata": {}

868+ },

869+ {

870+ "output_type": "display_data",

871+ "data": {

872+ "text/plain": [

873+ "tokenizer.model: 0%| | 0.00/500k [00:00<?, ?B/s]"

874+ ],

875+ "application/vnd.jupyter.widget-view+json": {

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877+ "version_minor": 0,

878+ "model_id": "16cdfd0f48804e2698f25fc1e8675f07"

879+ }

880+ },

881+ "metadata": {}

882+ },

883+ {

884+ "output_type": "display_data",

885+ "data": {

886+ "text/plain": [

887+ "tokenizer.json: 0%| | 0.00/1.84M [00:00<?, ?B/s]"

888+ ],

889+ "application/vnd.jupyter.widget-view+json": {

890+ "version_major": 2,

891+ "version_minor": 0,

892+ "model_id": "fe1f304135164f2983ba40f3f230c97a"

893+ }

894+ },

895+ "metadata": {}

896+ },

897+ {

898+ "output_type": "display_data",

899+ "data": {

900+ "text/plain": [

901+ "special_tokens_map.json: 0%| | 0.00/411 [00:00<?, ?B/s]"

902+ ],

903+ "application/vnd.jupyter.widget-view+json": {

904+ "version_major": 2,

905+ "version_minor": 0,

906+ "model_id": "b21b2de1e98a43c0a4a676d7159f95bb"

907+ }

908+ },

909+ "metadata": {}

910+ }

911+ ],

912+ "source": [

913+ "base_model = \"codellama/CodeLlama-7b-hf\"\n",

914+ "model = AutoModelForCausalLM.from_pretrained(\n",

915+ " base_model,\n",

916+ " load_in_8bit=True,\n",

917+ " torch_dtype=torch.float16,\n",

918+ " device_map=\"auto\",\n",

919+ ")\n",

920+ "tokenizer = AutoTokenizer.from_pretrained(\"codellama/CodeLlama-7b-hf\")"

921+ ]

922+ },

923+ {

924+ "cell_type": "markdown",

925+ "metadata": {

926+ "id": "F3OF-wtj1CSJ"

927+ },

928+ "source": [

929+ "torch_dtype=torch.float16 means computations are performed using a float16 representation, even though the values themselves are 8 bit ints.\n",

930+ "\n",

931+ "\n",

932+ "If you get error \"ValueError: Tokenizer class CodeLlamaTokenizer does not exist or is not currently imported.\" Make sure you have transformers version is 4.33.0.dev0 and accelerate is >=0.20.3.\n"

933+ ]

934+ },

935+ {

936+ "cell_type": "markdown",

937+ "metadata": {

938+ "id": "_2VXqJJe1CSJ"

939+ },

940+ "source": [

941+ "### 3. Check base model\n",

942+ "A very good common practice is to check whether a model can already do the task at hand. Fine-tuning is something you want to try to avoid at all cost:\n"

943+ ]

944+ },

945+ {

946+ "cell_type": "code",

947+ "execution_count": null,

948+ "metadata": {

949+ "colab": {

950+ "base_uri": "https://localhost:8080/"

951+ },

952+ "id": "uiyAff1a1CSJ",

953+ "outputId": "f4813f3b-8398-42b4-ecfd-e52267cdc4c6"

954+ },

955+ "outputs": [

956+ {

957+ "output_type": "stream",

958+ "name": "stderr",

959+ "text": [

960+ "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"

961+ ]

962+ },

963+ {

964+ "output_type": "stream",

965+ "name": "stdout",

966+ "text": [

967+ "You are a powerful purescript-to-javascript compiler. Your job is to convert purescript code into javascript. You are given \"Input\" in purescript, \"Context PS\" showing a similar chunk of PureScript and \"Context JS\" that shows you what \"Context PS\" compiles to in JS.\n",

968+ "\n",

969+ "You must output the JavaScript that corresponds to this purescript.\n",

970+ "### Input:\n",

971+ "module Snapshot.EffectBindAgain where\n",

972+ "\n",

973+ "import Prelude\n",

974+ "import Effect (Effect)\n",

975+ "import Effect.Class.Console as Console\n",

976+ "\n",

977+ "test45 :: Effect Unit\n",

978+ "test45 = do\n",

979+ " value1 <- Console.log \"foo\"\n",

980+ " let a = pure value1\n",

981+ " a\n",

982+ "\n",

983+ "### Context PS:\n",

984+ "module Snapshot.EffectBind01 where\n",

985+ "\n",

986+ "import Prelude\n",

987+ "import Effect (Effect)\n",

988+ "import Effect.Class.Console as Console\n",

989+ "\n",

990+ "test1 :: Effect Unit\n",

991+ "test1 = do\n",

992+ " Console.log \"1\"\n",

993+ " value <- Console.log \"2\"\n",

994+ " Console.log \"3\"\n",

995+ " pure value\n",

996+ "\n",

997+ "### Context JS:\n",

998+ "\n",

999+ "import * as Effect$dConsole from \"../Effect.Console/index.js\";\n",

1000+ "const test1 = /* #__PURE__ */ (() => {\n",

1001+ " const $0 = Effect$dConsole.log(\"1\");\n",

1002+ " return () => {\n",

1003+ " $0();\n",

1004+ " const value = Effect$dConsole.log(\"2\")();\n",

1005+ " Effect$dConsole.log(\"3\")();\n",

1006+ " return value;\n",

1007+ " };\n",

1008+ "})();\n",

1009+ "export {test1};\n",

1010+ "\n",

1011+ "### Response:\n",

1012+ "\n",

1013+ "import * as Effect$dConsole from \"../Effect.Console/index.js\";\n",

1014+ "const test45 = /* #__PURE__ */ (() => {\n",

1015+ " const $0 = Effect$dConsole.log(\"foo\");\n",

1016+ " return () => {\n",

1017+ " $0();\n",

1018+ " const a = Effect$dConsole.log(\"foo\")();\n",

1019+ " return a;\n",

1020+ " };\n",

1021+ "})();\n",

1022+ "export {test45};\n",

1023+ "\n",

1024+ "### Ex\n"

1025+ ]

1026+ }

1027+ ],

1028+ "source": [

1029+ "eval_prompt = \"\"\"You are a powerful purescript-to-javascript compiler. Your job is to convert purescript code into javascript. You are given \"Input\" in purescript, \"Context PS\" showing a similar chunk of PureScript and \"Context JS\" that shows you what \"Context PS\" compiles to in JS.\n",

1030+ "\n",

1031+ "You must output the JavaScript that corresponds to this purescript.\n",

1032+ "### Input:\n",

1033+ "module Snapshot.EffectBindAgain where\n",

1034+ "\n",

1035+ "import Prelude\n",

1036+ "import Effect (Effect)\n",

1037+ "import Effect.Class.Console as Console\n",

1038+ "\n",

1039+ "test45 :: Effect Unit\n",

1040+ "test45 = do\n",

1041+ " value1 <- Console.log \"foo\"\n",

1042+ " let a = pure value1\n",

1043+ " a\n",

1044+ "\n",

1045+ "### Context PS:\n",

1046+ "module Snapshot.EffectBind01 where\n",

1047+ "\n",

1048+ "import Prelude\n",

1049+ "import Effect (Effect)\n",

1050+ "import Effect.Class.Console as Console\n",

1051+ "\n",

1052+ "test1 :: Effect Unit\n",

1053+ "test1 = do\n",

1054+ " Console.log \"1\"\n",

1055+ " value <- Console.log \"2\"\n",

1056+ " Console.log \"3\"\n",

1057+ " pure value\n",

1058+ "\n",

1059+ "### Context JS:\n",

1060+ "\n",

1061+ "import * as Effect$dConsole from \"../Effect.Console/index.js\";\n",

1062+ "const test1 = /* #__PURE__ */ (() => {\n",

1063+ " const $0 = Effect$dConsole.log(\"1\");\n",

1064+ " return () => {\n",

1065+ " $0();\n",

1066+ " const value = Effect$dConsole.log(\"2\")();\n",

1067+ " Effect$dConsole.log(\"3\")();\n",

1068+ " return value;\n",

1069+ " };\n",

1070+ "})();\n",

1071+ "export {test1};\n",

1072+ "\n",

1073+ "### Response:\n",

1074+ "\"\"\"\n",

1075+ "# {'question': 'Name the comptroller for office of prohibition', 'context': 'CREATE TABLE table_22607062_1 (comptroller VARCHAR, ticket___office VARCHAR)', 'answer': 'SELECT comptroller FROM table_22607062_1 WHERE ticket___office = \"Prohibition\"'}\n",

1076+ "model_input = tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",

1077+ "\n",

1078+ "model.eval()\n",

1079+ "with torch.no_grad():\n",

1080+ " print(tokenizer.decode(model.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True))"

1081+ ]

1082+ },

1083+ {

1084+ "cell_type": "markdown",

1085+ "metadata": {

1086+ "id": "544rmsdU1CSJ"

1087+ },

1088+ "source": [

1089+ "I get the output:\n",

1090+ "```\n",

1091+ "SELECT * FROM table_name_12 WHERE class > 91.5 AND city_of_license = 'hyannis, nebraska'\n",

1092+ "```\n",

1093+ "which is clearly wrong if the input is asking for just class!"

1094+ ]

1095+ },

1096+ {

1097+ "cell_type": "markdown",

1098+ "metadata": {

1099+ "id": "puuOXL2R1CSJ"

1100+ },

1101+ "source": [

1102+ "### 4. Tokenization\n",

1103+ "Setup some tokenization settings like left padding because it makes [training use less memory](https://ai.stackexchange.com/questions/41485/while-fine-tuning-a-decoder-only-llm-like-llama-on-chat-dataset-what-kind-of-pa):"

1104+ ]

1105+ },

1106+ {

1107+ "cell_type": "code",

1108+ "execution_count": null,

1109+ "metadata": {

1110+ "id": "c1P0mphA1CSJ"

1111+ },

1112+ "outputs": [],

1113+ "source": [

1114+ "tokenizer.add_eos_token = True\n",

1115+ "tokenizer.pad_token_id = 0\n",

1116+ "tokenizer.padding_side = \"left\""

1117+ ]

1118+ },

1119+ {

1120+ "cell_type": "markdown",

1121+ "metadata": {

1122+ "id": "kpo66hMo1CSJ"

1123+ },

1124+ "source": [

1125+ "Setup the tokenize function to make labels and input_ids the same. This is basically what [self-supervised fine-tuning](https://neptune.ai/blog/self-supervised-learning) is:"

1126+ ]

1127+ },

1128+ {

1129+ "cell_type": "code",

1130+ "execution_count": null,

1131+ "metadata": {

1132+ "id": "gjSerml71CSJ"

1133+ },

1134+ "outputs": [],

1135+ "source": [

1136+ "def tokenize(prompt):\n",

1137+ " result = tokenizer(\n",

1138+ " prompt,\n",

1139+ " truncation=True,\n",

1140+ " max_length=512,\n",

1141+ " padding=False,\n",

1142+ " return_tensors=None,\n",

1143+ " )\n",

1144+ "\n",

1145+ " # \"self-supervised learning\" means the labels are also the inputs:\n",

1146+ " result[\"labels\"] = result[\"input_ids\"].copy()\n",

1147+ "\n",

1148+ " return result"

1149+ ]

1150+ },

1151+ {

1152+ "cell_type": "markdown",

1153+ "metadata": {

1154+ "id": "fNk_3THv1CSK"

1155+ },

1156+ "source": [

1157+ "And run convert each data_point into a prompt that I found online that works quite well:"

1158+ ]

1159+ },

1160+ {

1161+ "cell_type": "code",

1162+ "execution_count": null,

1163+ "metadata": {

1164+ "id": "MvO7A-ZF1CSK"

1165+ },

1166+ "outputs": [],

1167+ "source": [

1168+ "def generate_and_tokenize_prompt(data_point):\n",

1169+ " full_prompt =f\"\"\"You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question and context regarding one or more tables.\n",

1170+ "\n",

1171+ "You must output the SQL query that answers the question.\n",

1172+ "\n",

1173+ "### Input:\n",

1174+ "{data_point[\"question\"]}\n",

1175+ "\n",

1176+ "### Context:\n",

1177+ "{data_point[\"context\"]}\n",

1178+ "\n",

1179+ "### Response:\n",

1180+ "{data_point[\"answer\"]}\n",

1181+ "\"\"\"\n",

1182+ " return tokenize(full_prompt)"

1183+ ]

1184+ },

1185+ {

1186+ "cell_type": "markdown",

1187+ "metadata": {

1188+ "id": "sLvwo-ax1CSK"

1189+ },

1190+ "source": [

1191+ "Reformat to prompt and tokenize each sample:"

1192+ ]

1193+ },

1194+ {

1195+ "cell_type": "code",

1196+ "execution_count": null,

1197+ "metadata": {

1198+ "id": "SA2BqSzW1CSK"

1199+ },

1200+ "outputs": [],

1201+ "source": [

1202+ "tokenized_train_dataset = train_dataset.map(generate_and_tokenize_prompt)\n",

1203+ "tokenized_val_dataset = eval_dataset.map(generate_and_tokenize_prompt)"

1204+ ]

1205+ },

1206+ {

1207+ "cell_type": "markdown",

1208+ "metadata": {

1209+ "id": "Z5ByhitV1CSK"

1210+ },

1211+ "source": [

1212+ "### 5. Setup Lora"

1213+ ]

1214+ },

1215+ {

1216+ "cell_type": "code",

1217+ "execution_count": null,

1218+ "metadata": {

1219+ "id": "9q_26pz71CSK"

1220+ },

1221+ "outputs": [],

1222+ "source": [

1223+ "model.train() # put model back into training mode\n",

1224+ "model = prepare_model_for_int8_training(model)\n",

1225+ "\n",

1226+ "config = LoraConfig(\n",

1227+ " r=16,\n",

1228+ " lora_alpha=16,\n",

1229+ " target_modules=[\n",

1230+ " \"q_proj\",\n",

1231+ " \"k_proj\",\n",

1232+ " \"v_proj\",\n",

1233+ " \"o_proj\",\n",

1234+ "],\n",

1235+ " lora_dropout=0.05,\n",

1236+ " bias=\"none\",\n",

1237+ " task_type=\"CAUSAL_LM\",\n",

1238+ ")\n",

1239+ "model = get_peft_model(model, config)"

1240+ ]

1241+ },

1242+ {

1243+ "cell_type": "markdown",

1244+ "metadata": {

1245+ "id": "IvkQ3igC1CSK"

1246+ },

1247+ "source": [

1248+ "To resume from a checkpoint, set resume_from_checkpoint to the path of the adapter_model.bin you want to resume from. This code'll replace the lora adapter attached to the model:"

1249+ ]

1250+ },

1251+ {

1252+ "cell_type": "code",

1253+ "execution_count": null,

1254+ "metadata": {

1255+ "id": "TMTxR2JT1CSK"

1256+ },

1257+ "outputs": [],

1258+ "source": [

1259+ "resume_from_checkpoint = \"\" # set this to the adapter_model.bin file you want to resume from\n",

1260+ "\n",

1261+ "if resume_from_checkpoint:\n",

1262+ " if os.path.exists(resume_from_checkpoint):\n",

1263+ " print(f\"Restarting from {resume_from_checkpoint}\")\n",

1264+ " adapters_weights = torch.load(resume_from_checkpoint)\n",

1265+ " set_peft_model_state_dict(model, adapters_weights)\n",

1266+ " else:\n",

1267+ " print(f\"Checkpoint {resume_from_checkpoint} not found\")"

1268+ ]

1269+ },

1270+ {

1271+ "cell_type": "markdown",

1272+ "metadata": {

1273+ "id": "GW1wtvbP1CSK"

1274+ },

1275+ "source": [

1276+ "Optional stuff to setup Weights and Biases to view training graphs:"

1277+ ]

1278+ },

1279+ {

1280+ "cell_type": "code",

1281+ "execution_count": null,

1282+ "metadata": {

1283+ "id": "AC1_qWbS1CSK"

1284+ },

1285+ "outputs": [],

1286+ "source": [

1287+ "wandb_project = \"sql-try2-coder\"\n",

1288+ "if len(wandb_project) > 0:\n",

1289+ " os.environ[\"WANDB_PROJECT\"] = wandb_project\n"

1290+ ]

1291+ },

1292+ {

1293+ "cell_type": "code",

1294+ "execution_count": null,

1295+ "metadata": {

1296+ "id": "5iv4txxu1CSK"

1297+ },

1298+ "outputs": [],

1299+ "source": [

1300+ "if torch.cuda.device_count() > 1:\n",

1301+ " # keeps Trainer from trying its own DataParallelism when more than 1 gpu is available\n",

1302+ " model.is_parallelizable = True\n",

1303+ " model.model_parallel = True"

1304+ ]

1305+ },

1306+ {

1307+ "cell_type": "markdown",

1308+ "metadata": {

1309+ "id": "pYSsnciQ1CSK"

1310+ },

1311+ "source": [

1312+ "### 6. Training arguments\n",

1313+ "If you run out of GPU memory, change per_device_train_batch_size. The gradient_accumulation_steps variable should ensure this doesn't affect batch dynamics during the training run. All the other variables are standard stuff that I wouldn't recommend messing with:"

1314+ ]

1315+ },

1316+ {

1317+ "cell_type": "code",

1318+ "execution_count": null,

1319+ "metadata": {

1320+ "id": "uvEi1kP21CSK"

1321+ },

1322+ "outputs": [],

1323+ "source": [

1324+ "batch_size = 128\n",

1325+ "per_device_train_batch_size = 32\n",

1326+ "gradient_accumulation_steps = batch_size // per_device_train_batch_size\n",

1327+ "output_dir = \"sql-code-llama\"\n",

1328+ "\n",

1329+ "training_args = TrainingArguments(\n",

1330+ " per_device_train_batch_size=per_device_train_batch_size,\n",

1331+ " gradient_accumulation_steps=gradient_accumulation_steps,\n",

1332+ " warmup_steps=100,\n",

1333+ " max_steps=400,\n",

1334+ " learning_rate=3e-4,\n",

1335+ " fp16=True,\n",

1336+ " logging_steps=10,\n",

1337+ " optim=\"adamw_torch\",\n",

1338+ " evaluation_strategy=\"steps\", # if val_set_size > 0 else \"no\",\n",

1339+ " save_strategy=\"steps\",\n",

1340+ " eval_steps=20,\n",

1341+ " save_steps=20,\n",

1342+ " output_dir=output_dir,\n",

1343+ " # save_total_limit=3,\n",

1344+ " load_best_model_at_end=False,\n",

1345+ " # ddp_find_unused_parameters=False if ddp else None,\n",

1346+ " group_by_length=True, # group sequences of roughly the same length together to speed up training\n",

1347+ " report_to=\"wandb\", # if use_wandb else \"none\",\n",

1348+ " run_name=f\"codellama-{datetime.now().strftime('%Y-%m-%d-%H-%M')}\", # if use_wandb else None,\n",

1349+ " )\n",

1350+ "\n",

1351+ "trainer = Trainer(\n",

1352+ " model=model,\n",

1353+ " train_dataset=tokenized_train_dataset,\n",

1354+ " eval_dataset=tokenized_val_dataset,\n",

1355+ " args=training_args,\n",

1356+ " data_collator=DataCollatorForSeq2Seq(\n",

1357+ " tokenizer, pad_to_multiple_of=8, return_tensors=\"pt\", padding=True\n",

1358+ " ),\n",

1359+ ")"

1360+ ]

1361+ },

1362+ {

1363+ "cell_type": "markdown",

1364+ "metadata": {

1365+ "id": "1aJp6Jxl1CSK"

1366+ },

1367+ "source": [

1368+ "Then we do some pytorch-related optimisation (which just make training faster but don't affect accuracy):"

1369+ ]

1370+ },

1371+ {

1372+ "cell_type": "code",

1373+ "execution_count": null,

1374+ "metadata": {

1375+ "id": "6ycCHZZl1CSK"

1376+ },

1377+ "outputs": [],

1378+ "source": [

1379+ "model.config.use_cache = False\n",

1380+ "\n",

1381+ "old_state_dict = model.state_dict\n",

1382+ "model.state_dict = (lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())).__get__(\n",

1383+ " model, type(model)\n",

1384+ ")\n",

1385+ "if torch.__version__ >= \"2\" and sys.platform != \"win32\":\n",

1386+ " print(\"compiling the model\")\n",

1387+ " model = torch.compile(model)"

1388+ ]

1389+ },

1390+ {

1391+ "cell_type": "code",

1392+ "execution_count": null,

1393+ "metadata": {

1394+ "id": "bF5oWKxK1CSL"

1395+ },

1396+ "outputs": [],

1397+ "source": [

1398+ "trainer.train()"

1399+ ]

1400+ },

1401+ {

1402+ "cell_type": "markdown",

1403+ "metadata": {

1404+ "id": "j1dRQLMT1CSU"

1405+ },

1406+ "source": [

1407+ "### Load the final checkpoint\n",

1408+ "Now for the moment of truth! Has our work paid off...?"

1409+ ]

1410+ },

1411+ {

1412+ "cell_type": "code",

1413+ "execution_count": null,

1414+ "metadata": {

1415+ "id": "sRdVgDTg1CSU"

1416+ },

1417+ "outputs": [],

1418+ "source": [

1419+ "import torch\n",

1420+ "from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer\n",

1421+ "\n",

1422+ "base_model = \"codellama/CodeLlama-7b-hf\"\n",

1423+ "model = AutoModelForCausalLM.from_pretrained(\n",

1424+ " base_model,\n",

1425+ " load_in_8bit=True,\n",

1426+ " torch_dtype=torch.float16,\n",

1427+ " device_map=\"auto\",\n",

1428+ ")\n",

1429+ "tokenizer = AutoTokenizer.from_pretrained(\"codellama/CodeLlama-7b-hf\")"

1430+ ]

1431+ },

1432+ {

1433+ "cell_type": "markdown",

1434+ "metadata": {

1435+ "id": "76UlHzhy1CSU"

1436+ },

1437+ "source": [

1438+ "To load a fine-tuned Lora/Qlora adapter use PeftModel.from_pretrained. ```output_dir``` should be something containing an adapter_config.json and adapter_model.bin:"

1439+ ]

1440+ },

1441+ {

1442+ "cell_type": "code",

1443+ "execution_count": null,

1444+ "metadata": {

1445+ "id": "rQrCR0os1CSU"

1446+ },

1447+ "outputs": [],

1448+ "source": [

1449+ "from peft import PeftModel\n",

1450+ "model = PeftModel.from_pretrained(model, output_dir)"

1451+ ]

1452+ },

1453+ {

1454+ "cell_type": "markdown",

1455+ "metadata": {

1456+ "id": "roqy_WRi1CSU"

1457+ },

1458+ "source": [

1459+ "Try the same prompt as before:"

1460+ ]

1461+ },

1462+ {

1463+ "cell_type": "code",

1464+ "execution_count": null,

1465+ "metadata": {

1466+ "id": "GrCxouNp1CSU"

1467+ },

1468+ "outputs": [],

1469+ "source": [

1470+ "eval_prompt = \"\"\"You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question and context regarding one or more tables.\n",

1471+ "\n",

1472+ "You must output the SQL query that answers the question.\n",

1473+ "### Input:\n",

1474+ "Which Class has a Frequency MHz larger than 91.5, and a City of license of hyannis, nebraska?\n",

1475+ "\n",

1476+ "### Context:\n",

1477+ "CREATE TABLE table_name_12 (class VARCHAR, frequency_mhz VARCHAR, city_of_license VARCHAR)\n",

1478+ "\n",

1479+ "### Response:\n",

1480+ "\"\"\"\n",

1481+ "\n",

1482+ "model_input = tokenizer(eval_prompt, return_tensors=\"pt\").to(\"cuda\")\n",

1483+ "\n",

1484+ "model.eval()\n",

1485+ "with torch.no_grad():\n",

1486+ " print(tokenizer.decode(model.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True))\n"

1487+ ]

1488+ },

1489+ {

1490+ "cell_type": "markdown",

1491+ "metadata": {

1492+ "id": "I6IcTOCq1CSU"

1493+ },

1494+ "source": [

1495+ "And the model outputs:\n",

1496+ "```\n",

1497+ "SELECT class FROM table_name_12 WHERE frequency_mhz > 91.5 AND city_of_license = \"hyannis, nebraska\"\n",

1498+ "```\n",

1499+ "So it works! If you want to convert your this adapter to a Llama.cpp model to run locally follow my other [guide](https://ragntune.com/blog/A-guide-to-running-Llama-2-qlora-loras-on-Llama.cpp). If you have any questions, shoot me a message on [Elon Musk's website](https://twitter.com/samlhuillier_).\n"

1500+ ]

1501+ }

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