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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2023-12-21T18:16:54.952190Z",
"iopub.status.busy": "2023-12-21T18:16:54.951899Z",
"iopub.status.idle": "2023-12-21T18:17:53.065431Z",
"shell.execute_reply": "2023-12-21T18:17:53.064016Z",
"shell.execute_reply.started": "2023-12-21T18:16:54.952164Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting git+https://github.com/huggingface/peft.git\n",
" Cloning https://github.com/huggingface/peft.git to /tmp/pip-req-build-dpzetz7o\n",
" Running command git clone --filter=blob:none --quiet https://github.com/huggingface/peft.git /tmp/pip-req-build-dpzetz7o\n",
" Resolved https://github.com/huggingface/peft.git to commit 993836ff90791289b94d27caa46385eec958e147\n",
" Installing build dependencies ... \u001b[?25ldone\n",
"\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n",
"\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
"\u001b[?25hCollecting trl\n",
" Obtaining dependency information for trl from https://files.pythonhosted.org/packages/0d/44/c406c3cf5981bddb16ff72acb5ca235888db4073d868cf51bd143bef3aad/trl-0.7.4-py3-none-any.whl.metadata\n",
" Downloading trl-0.7.4-py3-none-any.whl.metadata (10 kB)\n",
"Requirement already satisfied: transformers in /opt/conda/lib/python3.10/site-packages (4.36.0)\n",
"Collecting transformers\n",
" Obtaining dependency information for transformers from https://files.pythonhosted.org/packages/20/0a/739426a81f7635b422fbe6cb8d1d99d1235579a6ac8024c13d743efa6847/transformers-4.36.2-py3-none-any.whl.metadata\n",
" Downloading transformers-4.36.2-py3-none-any.whl.metadata (126 kB)\n",
"\u001b[2K \u001b[90m鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹乗u001b[0m \u001b[32m126.8/126.8 kB\u001b[0m \u001b[31m670.8 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: accelerate in /opt/conda/lib/python3.10/site-packages (0.25.0)\n",
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"Requirement already satisfied: datasets in /opt/conda/lib/python3.10/site-packages (from trl) (2.1.0)\n",
"Collecting tyro>=0.5.11 (from trl)\n",
" Obtaining dependency information for tyro>=0.5.11 from https://files.pythonhosted.org/packages/c5/11/abdf67467d06713b431618732a43f82d1b1f02120107b05a789afbcdf54d/tyro-0.6.0-py3-none-any.whl.metadata\n",
" Downloading tyro-0.6.0-py3-none-any.whl.metadata (7.5 kB)\n",
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"Requirement already satisfied: tokenizers<0.19,>=0.14 in /opt/conda/lib/python3.10/site-packages (from transformers) (0.15.0)\n",
"Requirement already satisfied: safetensors>=0.3.1 in /opt/conda/lib/python3.10/site-packages (from transformers) (0.4.1)\n",
"Requirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.10/site-packages (from transformers) (4.66.1)\n",
"Requirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from accelerate) (5.9.3)\n",
"Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (2023.12.2)\n",
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"Requirement already satisfied: rich>=11.1.0 in /opt/conda/lib/python3.10/site-packages (from tyro>=0.5.11->trl) (13.5.2)\n",
"Collecting shtab>=1.5.6 (from tyro>=0.5.11->trl)\n",
" Obtaining dependency information for shtab>=1.5.6 from https://files.pythonhosted.org/packages/40/ad/7227da64498eaa7abecee4311008f70869e156014b3270cec36e2e70cd31/shtab-1.6.5-py3-none-any.whl.metadata\n",
" Downloading shtab-1.6.5-py3-none-any.whl.metadata (7.3 kB)\n",
"Requirement already satisfied: pyarrow>=5.0.0 in /opt/conda/lib/python3.10/site-packages (from datasets->trl) (11.0.0)\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->transformers) (3.2.0)\n",
"Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->transformers) (3.4)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->transformers) (1.26.15)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->transformers) (2023.11.17)\n",
"Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets->trl) (23.1.0)\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets->trl) (6.0.4)\n",
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"Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets->trl) (1.9.2)\n",
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"Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets->trl) (1.3.1)\n",
"Requirement already satisfied: markdown-it-py>=2.2.0 in /opt/conda/lib/python3.10/site-packages (from rich>=11.1.0->tyro>=0.5.11->trl) (3.0.0)\n",
"Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /opt/conda/lib/python3.10/site-packages (from rich>=11.1.0->tyro>=0.5.11->trl) (2.16.1)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.4.0->trl) (2.1.3)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets->trl) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets->trl) (2023.3)\n",
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"Requirement already satisfied: mdurl~=0.1 in /opt/conda/lib/python3.10/site-packages (from markdown-it-py>=2.2.0->rich>=11.1.0->tyro>=0.5.11->trl) (0.1.0)\n",
"Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas->datasets->trl) (1.16.0)\n",
"Downloading trl-0.7.4-py3-none-any.whl (133 kB)\n",
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"\u001b[?25hDownloading transformers-4.36.2-py3-none-any.whl (8.2 MB)\n",
"\u001b[2K \u001b[90m鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣\u001b[0m \u001b[32m8.2/8.2 MB\u001b[0m \u001b[31m19.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
"\u001b[?25hDownloading tyro-0.6.0-py3-none-any.whl (100 kB)\n",
"\u001b[2K \u001b[90m鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣\u001b[0m \u001b[32m100.9/100.9 kB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hDownloading shtab-1.6.5-py3-none-any.whl (13 kB)\n",
"Building wheels for collected packages: peft\n",
" Building wheel for peft (pyproject.toml) ... \u001b[?25ldone\n",
"\u001b[?25h Created wheel for peft: filename=peft-0.7.2.dev0-py3-none-any.whl size=169329 sha256=65c9f890817815f066ee515202e5f5044739b6cb22fcf4ef4280bc3ee8339237\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-fsxcdvsj/wheels/d7/c7/de/1368fac8590e1b103ddc2ec2a28ad51d83aded1a3830e8a087\n",
"Successfully built peft\n",
"Installing collected packages: shtab, tyro, transformers, trl, peft\n",
" Attempting uninstall: transformers\n",
" Found existing installation: transformers 4.36.0\n",
" Uninstalling transformers-4.36.0:\n",
" Successfully uninstalled transformers-4.36.0\n",
"Successfully installed peft-0.7.2.dev0 shtab-1.6.5 transformers-4.36.2 trl-0.7.4 tyro-0.6.0\n",
"Requirement already satisfied: datasets in /opt/conda/lib/python3.10/site-packages (2.1.0)\n",
"Collecting datasets\n",
" Obtaining dependency information for datasets from https://files.pythonhosted.org/packages/e2/cf/db41e572d7ed958e8679018f8190438ef700aeb501b62da9e1eed9e4d69a/datasets-2.15.0-py3-none-any.whl.metadata\n",
" Downloading datasets-2.15.0-py3-none-any.whl.metadata (20 kB)\n",
"Collecting bitsandbytes\n",
" Obtaining dependency information for bitsandbytes from https://files.pythonhosted.org/packages/d9/8d/b62d4fb02587e293e5b91b68bbcaa2d88c6a0360b622e9521d4bd07a20cd/bitsandbytes-0.41.3.post2-py3-none-any.whl.metadata\n",
" Downloading bitsandbytes-0.41.3.post2-py3-none-any.whl.metadata (9.8 kB)\n",
"Collecting einops\n",
" Obtaining dependency information for einops from https://files.pythonhosted.org/packages/29/0b/2d1c0ebfd092e25935b86509a9a817159212d82aa43d7fb07eca4eeff2c2/einops-0.7.0-py3-none-any.whl.metadata\n",
" Downloading einops-0.7.0-py3-none-any.whl.metadata (13 kB)\n",
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"Collecting pyarrow-hotfix (from datasets)\n",
" Obtaining dependency information for pyarrow-hotfix from https://files.pythonhosted.org/packages/e4/f4/9ec2222f5f5f8ea04f66f184caafd991a39c8782e31f5b0266f101cb68ca/pyarrow_hotfix-0.6-py3-none-any.whl.metadata\n",
" Downloading pyarrow_hotfix-0.6-py3-none-any.whl.metadata (3.6 kB)\n",
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"Collecting fsspec[http]<=2023.10.0,>=2023.1.0 (from datasets)\n",
" Obtaining dependency information for fsspec[http]<=2023.10.0,>=2023.1.0 from https://files.pythonhosted.org/packages/e8/f6/3eccfb530aac90ad1301c582da228e4763f19e719ac8200752a4841b0b2d/fsspec-2023.10.0-py3-none-any.whl.metadata\n",
" Downloading fsspec-2023.10.0-py3-none-any.whl.metadata (6.8 kB)\n",
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"Downloading datasets-2.15.0-py3-none-any.whl (521 kB)\n",
"\u001b[2K \u001b[90m鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣鈹佲攣\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m22.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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"\u001b[?25hDownloading pyarrow_hotfix-0.6-py3-none-any.whl (7.9 kB)\n",
"Downloading fsspec-2023.10.0-py3-none-any.whl (166 kB)\n",
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"\u001b[?25hInstalling collected packages: bitsandbytes, pyarrow-hotfix, fsspec, einops, datasets\n",
" Attempting uninstall: fsspec\n",
" Found existing installation: fsspec 2023.12.2\n",
" Uninstalling fsspec-2023.12.2:\n",
" Successfully uninstalled fsspec-2023.12.2\n",
" Attempting uninstall: datasets\n",
" Found existing installation: datasets 2.1.0\n",
" Uninstalling datasets-2.1.0:\n",
" Successfully uninstalled datasets-2.1.0\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"cudf 23.8.0 requires cupy-cuda11x>=12.0.0, which is not installed.\n",
"cuml 23.8.0 requires cupy-cuda11x>=12.0.0, which is not installed.\n",
"dask-cudf 23.8.0 requires cupy-cuda11x>=12.0.0, which is not installed.\n",
"cudf 23.8.0 requires pandas<1.6.0dev0,>=1.3, but you have pandas 2.0.3 which is incompatible.\n",
"cudf 23.8.0 requires protobuf<5,>=4.21, but you have protobuf 3.20.3 which is incompatible.\n",
"cuml 23.8.0 requires dask==2023.7.1, but you have dask 2023.12.0 which is incompatible.\n",
"cuml 23.8.0 requires distributed==2023.7.1, but you have distributed 2023.12.0 which is incompatible.\n",
"dask-cuda 23.8.0 requires dask==2023.7.1, but you have dask 2023.12.0 which is incompatible.\n",
"dask-cuda 23.8.0 requires distributed==2023.7.1, but you have distributed 2023.12.0 which is incompatible.\n",
"dask-cuda 23.8.0 requires pandas<1.6.0dev0,>=1.3, but you have pandas 2.0.3 which is incompatible.\n",
"dask-cudf 23.8.0 requires dask==2023.7.1, but you have dask 2023.12.0 which is incompatible.\n",
"dask-cudf 23.8.0 requires distributed==2023.7.1, but you have distributed 2023.12.0 which is incompatible.\n",
"dask-cudf 23.8.0 requires pandas<1.6.0dev0,>=1.3, but you have pandas 2.0.3 which is incompatible.\n",
"gcsfs 2023.6.0 requires fsspec==2023.6.0, but you have fsspec 2023.10.0 which is incompatible.\n",
"raft-dask 23.8.0 requires dask==2023.7.1, but you have dask 2023.12.0 which is incompatible.\n",
"raft-dask 23.8.0 requires distributed==2023.7.1, but you have distributed 2023.12.0 which is incompatible.\n",
"s3fs 2023.12.2 requires fsspec==2023.12.2, but you have fsspec 2023.10.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed bitsandbytes-0.41.3.post2 datasets-2.15.0 einops-0.7.0 fsspec-2023.10.0 pyarrow-hotfix-0.6\n"
]
}
],
"source": [
"!pip install -U trl transformers accelerate git+https://github.com/huggingface/peft.git\n",
"!pip install -U datasets bitsandbytes einops wandb\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-21T18:22:23.573426Z",
"iopub.status.busy": "2023-12-21T18:22:23.572634Z",
"iopub.status.idle": "2023-12-21T18:22:52.776357Z",
"shell.execute_reply": "2023-12-21T18:22:52.775443Z",
"shell.execute_reply.started": "2023-12-21T18:22:23.573393Z"
}
},
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{
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},
{
"name": "stderr",
"output_type": "stream",
"text": [
"A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-2:\n",
"- configuration_phi.py\n",
". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
]
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{
"name": "stderr",
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"text": [
"A new version of the following files was downloaded from https://huggingface.co/microsoft/phi-2:\n",
"- modeling_phi.py\n",
". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
]
},
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},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
" warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
]
},
{
"data": {
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]
},
"metadata": {},
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"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"PhiForCausalLM(\n",
" (transformer): PhiModel(\n",
" (embd): Embedding(\n",
" (wte): Embedding(51200, 2560)\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (h): ModuleList(\n",
" (0-31): 32 x ParallelBlock(\n",
" (ln): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
" (mixer): MHA(\n",
" (rotary_emb): RotaryEmbedding()\n",
" (Wqkv): Linear4bit(in_features=2560, out_features=7680, bias=True)\n",
" (out_proj): Linear4bit(in_features=2560, out_features=2560, bias=True)\n",
" (inner_attn): SelfAttention(\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" (inner_cross_attn): CrossAttention(\n",
" (drop): Dropout(p=0.0, inplace=False)\n",
" )\n",
" )\n",
" (mlp): MLP(\n",
" (fc1): Linear4bit(in_features=2560, out_features=10240, bias=True)\n",
" (fc2): Linear4bit(in_features=10240, out_features=2560, bias=True)\n",
" (act): NewGELUActivation()\n",
" )\n",
" )\n",
" )\n",
" )\n",
" (lm_head): CausalLMHead(\n",
" (ln): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
" (linear): Linear(in_features=2560, out_features=51200, bias=True)\n",
" )\n",
" (loss): CausalLMLoss(\n",
" (loss_fct): CrossEntropyLoss()\n",
" )\n",
")\n"
]
}
],
"source": [
"import torch\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n",
"\n",
"#model_name = \"ybelkada/falcon-7b-sharded-bf16\"\n",
"model_name = \"microsoft/phi-2\"\n",
"bnb_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_quant_type=\"nf4\",\n",
" bnb_4bit_compute_dtype=torch.float16,\n",
")\n",
"\n",
"model = AutoModelForCausalLM.from_pretrained(\n",
" model_name,\n",
" quantization_config=bnb_config,\n",
" trust_remote_code=True\n",
")\n",
"model.config.use_cache = False\n",
"print(model)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-21T18:22:59.972504Z",
"iopub.status.busy": "2023-12-21T18:22:59.971964Z",
"iopub.status.idle": "2023-12-21T18:23:02.356756Z",
"shell.execute_reply": "2023-12-21T18:23:02.355664Z",
"shell.execute_reply.started": "2023-12-21T18:22:59.972462Z"
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},
"outputs": [
{
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]
},
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
]
}
],
"source": [
"tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
"tokenizer.pad_token = tokenizer.eos_token"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-21T18:23:12.232728Z",
"iopub.status.busy": "2023-12-21T18:23:12.231764Z",
"iopub.status.idle": "2023-12-21T18:23:12.276671Z",
"shell.execute_reply": "2023-12-21T18:23:12.275949Z",
"shell.execute_reply.started": "2023-12-21T18:23:12.232691Z"
}
},
"outputs": [],
"source": [
"from peft import LoraConfig\n",
"\n",
"lora_alpha = 16\n",
"lora_dropout = 0.1\n",
"lora_r = 64\n",
"\n",
"'''target_modules = [\n",
" \"query_key_value\",#Wqkv\n",
" \"dense\",#out_proj\n",
" \"dense_h_to_4h\", #fc1\n",
" \"dense_4h_to_h\", #fc2\n",
"]'''\n",
"\n",
"target_modules = [\n",
" \"Wqkv\",\n",
" \"out_proj\",\n",
" \"fc1\",\n",
" \"fc2\"\n",
"]\n",
"\n",
"peft_config = LoraConfig(\n",
" lora_alpha=lora_alpha,\n",
" lora_dropout=lora_dropout,\n",
" r=lora_r,\n",
" bias=\"none\",\n",
" task_type=\"CAUSAL_LM\",\n",
" target_modules=target_modules\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-21T18:23:20.097392Z",
"iopub.status.busy": "2023-12-21T18:23:20.096964Z",
"iopub.status.idle": "2023-12-21T18:23:26.215631Z",
"shell.execute_reply": "2023-12-21T18:23:26.214868Z",
"shell.execute_reply.started": "2023-12-21T18:23:20.097362Z"
}
},
"outputs": [
{
"data": {
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]
},
"metadata": {},
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"metadata": {},
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},
{
"data": {
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{
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from datasets import load_dataset\n",
"\n",
"#dataset_name = \"timdettmers/openassistant-guanaco\"\n",
"dataset_name = \"OpenAssistant/oasst1\"\n",
"full_dataset = load_dataset(dataset_name, split=\"train\")\n"
]
},
{
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"text": [
"{'text': '### Human: Escribir un cuento para dormir dirigido a ni帽os de entre 5 y 8 a帽os### Assistant: Hab铆a una vez un peque帽o rat贸n llamado Miguel que viv铆a en un agujero en una pared de piedra. Miguel era un rat贸n muy curioso, y le encantaba explorar su entorno en busca de aventuras emocionantes.\\n\\nUn d铆a, mientras Miguel exploraba los alrededores, encontr贸 un peque帽o agujero en la pared que no hab铆a visto antes. Decidi贸 investigar y se arrastr贸 por el agujero hasta llegar a un cuarto muy oscuro y tenebroso.\\n\\nMiguel estaba muy asustado, pero tambi茅n muy curioso, as铆 que decidi贸 explorar un poco m谩s. Pronto, encontr贸 una vieja caja de madera que parec铆a estar abandonada. Con mucho cuidado, Miguel abri贸 la caja, y dentro encontr贸 una hermosa varita m谩gica.\\n\\nMiguel nunca hab铆a visto nada igual antes, y estaba emocionado por su descubrimiento. Decidi贸 llevar la varita m谩gica de vuelta a su agujero en la pared y esconderla all铆.\\n\\nLa noche siguiente, Miguel no pod铆a dormir pensando en la varita m谩gica. Finalmente, se acerc贸 a la varita y la agarr贸 con sus peque帽as patitas. Para su sorpresa, la varita empez贸 a brillar con una luz m谩gica y una hada apareci贸 ante 茅l.\\n\\nEl hada le dijo a Miguel que era un rat贸n muy especial por haber encontrado la varita m谩gica, y que le conceder铆a un deseo. Miguel pens贸 en todas las cosas que quer铆a, pero finalmente decidi贸 que lo que m谩s quer铆a era tener amigos.\\n\\nEl hada sonri贸 y movi贸 la varita m谩gica. De repente, un grupo de ratones de todas las formas y tama帽os aparecieron ante Miguel. Todos eran amables y divertidos, y Miguel estaba muy feliz de haber hecho nuevos amigos.\\n\\nDesde entonces, Miguel y sus nuevos amigos se reun铆an todas las noches para jugar y contar historias. Y as铆, Miguel aprendi贸 que la amistad y la aventura pueden encontrarse en los lugares m谩s inesperados. Y con una sonrisa en el rostro, Miguel cerr贸 los ojos y se qued贸 dormido, so帽ando con las pr贸ximas aventuras que tendr铆a con sus nuevos amigos.### Human: Me ha encantado tu cuento 驴Podr铆as reescribirme el cuento pero escribiendo una historia que no contenga magia?### Assistant: 隆Me alegro que te haya encantado! Aqu铆 va otro cuento 隆Espero que te agrade!\\n\\nHab铆a una vez un peque帽o pueblo en el que viv铆an muchos animales. Hab铆a conejos, zorros, p谩jaros y muchos otros m谩s. Un d铆a, un peque帽o conejito llamado Ben decidi贸 explorar el bosque cercano a su casa.\\n\\nMientras caminaba, vio a un peque帽o pajarito que estaba muy triste. Ben se acerc贸 y le pregunt贸 qu茅 le suced铆a. El pajarito le explic贸 que hab铆a perdido su nido y que no sab铆a d贸nde estaba su familia.\\n\\nBen, sintiendo mucha empat铆a por el pajarito, decidi贸 ayudarlo a encontrar su nido. Juntos, buscaron en todos los 谩rboles y arbustos del bosque. Despu茅s de mucho buscar, encontraron el nido del pajarito. Estaba en un 谩rbol muy alto y parec铆a que hab铆a sido arrastrado por el viento.\\n\\nBen y el pajarito trabajaron juntos para llevar el nido de regreso al 谩rbol y asegurarlo all铆. Cuando terminaron, el pajarito estaba muy agradecido y le dijo a Ben que lo consideraba un amigo para siempre.\\n\\nDesde ese d铆a, Ben y el pajarito se convirtieron en amigos inseparables. Juntos exploraron el bosque, hicieron nuevos amigos y vivieron muchas aventuras emocionantes.\\n\\nFinalmente, lleg贸 el momento de dormir y Ben y el pajarito regresaron a sus hogares. Mientras se acurrucaba en su cama, Ben se sinti贸 muy feliz de haber encontrado un amigo tan especial. Y mientras cerraba los ojos, se dio cuenta de que la amistad era la magia m谩s poderosa de todas.### Human: Te he pedido que me reescribas el mismo cuento sin magia, no que inventaras un cuento nuevo.### Assistant: Hab铆a una vez un peque帽o rat贸n llamado Miguel que viv铆a en un agujero en una pared de piedra. Miguel era un rat贸n muy curioso, y le encantaba explorar su entorno en busca de aventuras emocionantes.\\n\\nUn d铆a, mientras Miguel exploraba los alrededores, encontr贸 un peque帽o agujero en la pared que no hab铆a visto antes. Decidi贸 investigar y se arrastr贸 por el agujero hasta llegar a un cuarto muy oscuro y tenebroso.\\n\\nMiguel estaba muy asustado, pero tambi茅n muy curioso, as铆 que decidi贸 explorar un poco m谩s. Pronto, encontr贸 una vieja caja de madera que parec铆a estar abandonada. Con mucho cuidado, Miguel abri贸 la caja, y dentro encontr贸 un objeto muy extra帽o para 茅l (no para nosotros los humanos).\\n\\nMiguel nunca hab铆a visto nada igual antes, y estaba emocionado por su descubrimiento. Decidi贸 llevar el objeto de vuelta a su agujero en la pared y esconderlo all铆.\\n\\nLa noche siguiente, Miguel no pod铆a dormir pensando en su descubrimiento. Finalmente, se acerc贸 al objeto y lo agarr贸 con sus peque帽as patitas. Para su sorpresa, el objeto empez贸 a brillar con una luz y una idea apareci贸 ante 茅l (es una l谩mpara port谩til, pero, no se lo digan).\\n\\nLa idea le dec铆a a Miguel que era un rat贸n muy especial por haber encontrado algo as铆, y que le propon铆a una actividad. Miguel pens贸 en todas las cosas que podr铆a hacer, pero finalmente decidi贸 que lo que m谩s quer铆a era conocer a otros ratones como 茅l, y con ello.\\n\\nSu interior sonri贸 y Miguel empez贸 a reflexionar en c贸mo podr铆a hacer para conocer a otros ratones. Decidi贸 que podr铆a invitar a los ratones de los alrededores a su agujero en la pared, atray茅ndolos con el objeto extra帽o para jugar y contar historias.\\n\\nDesde entonces, Miguel encend铆a la l谩mpara para llamar a sus nuevos amigos, se reun铆an todas las noches para jugar y contar historias. Y as铆, Miguel aprendi贸 que la amistad y la aventura pueden encontrarse en los lugares m谩s inesperados, y que los artilugios traen las soluciones m谩s inesperadas. Y con una sonrisa en el rostro, Miguel cerr贸 los ojos y se qued贸 dormido, so帽ando con las pr贸ximas aventuras que tendr铆a con sus nuevos amigos.\\n\\nEsperemos que la bater铆a de la l谩mpara port谩til nunca se acabe, pero, lo bueno es que ya todos se conocen, y nunca m谩s necesitara usar la l谩mpara.\\n\\nEllos fueron amigos por siempre...\\n\\nEspero que haya sido de ayuda, si tienes alguna otra pregunta o necesitas algo m谩s, no dudes en consultar.### Assistant: Hab铆a una vez un peque帽o zorro llamado Max, que viv铆a en un bosque m谩gico rodeado de 谩rboles enormes y un r铆o cristalino. Max era un zorro muy curioso y le encantaba explorar su hogar en busca de aventuras. Una noche, mientras se preparaba para dormir, Max escuch贸 un ruido extra帽o que ven铆a del bosque. Curioso como era, decidi贸 investigar.\\n\\nAl salir de su madriguera, Max se dio cuenta de que el ruido ven铆a de un 谩rbol cercano. Al acercarse, encontr贸 a un peque帽o b煤ho atrapado entre las ramas. Max se acerc贸 y le pregunt贸 al b煤ho qu茅 le hab铆a pasado. El b煤ho explic贸 que hab铆a estado cazando ratones y que accidentalmente se hab铆a enredado en las ramas del 谩rbol.\\n\\nMax no dud贸 en ayudar al b煤ho. Con su agilidad y astucia, logr贸 desenredar al peque帽o b煤ho y lo llev贸 de regreso a su nido en lo alto del 谩rbol. El b煤ho estaba muy agradecido y le prometi贸 a Max que si alguna vez necesitaba ayuda, estar铆a all铆 para 茅l.\\n\\nA partir de ese d铆a, Max y el b煤ho se convirtieron en amigos inseparables. Max aprendi贸 mucho del sabio b煤ho y juntos exploraron el bosque en busca de nuevas aventuras. Ya no se sent铆a solo en su madriguera, sab铆a que ten铆a un amigo en quien pod铆a confiar.\\n\\nLa noche que Max hab铆a salvado al b煤ho, mientras regresaban a casa, un espect谩culo de luces apareci贸 en el cielo, parec铆a una lluvia de estrellas. El b煤ho le cont贸 a Max que esto solo suced铆a una vez al a帽o, y que significaba que hab铆a magia en el aire.\\n\\nMax se dio cuenta de que la magia ya estaba en su vida, porque hab铆a encontrado a un amigo verdadero. Y as铆, acurrucados en su madriguera, Max y el b煤ho se quedaron dormidos bajo la lluvia de estrellas, con la seguridad de que su amistad los llevar铆a a muchas m谩s aventuras y descubrimientos en el bosque m谩gico.### Human: Escribe el mismo cuento, pero a帽adiendo di谩logos### Assistant: Hab铆a una vez un peque帽o zorro llamado Max, que viv铆a en un bosque m谩gico rodeado de 谩rboles enormes y un r铆o cristalino. Max era un zorro muy curioso y le encantaba explorar su hogar en busca de aventuras. Una noche, mientras se preparaba para dormir, Max escuch贸 un ruido extra帽o que ven铆a del bosque. Curioso como era, decidi贸 investigar.\\n\\n\"驴Qu茅 ser谩 ese ruido?\" - se pregunt贸 Max en voz alta.\\n\\n\"驴Qu茅 pasa, Max?\" - pregunt贸 su amigo b煤ho, quien hab铆a estado despierto.\\n\\n\"Escuch茅 un ruido extra帽o y fui a investigar\" - respondi贸 Max.\\n\\n\"驴Quieres que te acompa帽e?\" - ofreci贸 el b煤ho.\\n\\nMax acept贸, y juntos se dirigieron hacia el 谩rbol donde proven铆a el ruido. Al acercarse, encontraron al peque帽o b煤ho atrapado entre las ramas.\\n\\n\"驴Qu茅 ha pasado aqu铆?\" - pregunt贸 el b煤ho al ver al peque帽o atrapado.\\n\\n\"He estado cazando ratones y me enred茅 en las ramas del 谩rbol\" - explic贸 el peque帽o b煤ho.\\n\\n\"隆No te preocupes! Vamos a ayudarte a salir de ah铆\" - dijo Max decidido.\\n\\nCon su agilidad y astucia, Max logr贸 desenredar al peque帽o b煤ho y lo llev贸 de regreso a su nido en lo alto del 谩rbol.\\n\\n\"隆Gracias por ayudarme! No s茅 c贸mo podr铆a haber salido de ah铆 sin ti\" - agradeci贸 el peque帽o b煤ho.\\n\\n\"隆No hay problema! Eso es lo que hacen los amigos\" - respondi贸 Max con una sonrisa.\\n\\nA partir de ese d铆a, Max y el b煤ho se convirtieron en amigos inseparables. Juntos exploraron el bosque en busca de nuevas aventuras y descubrieron cosas fascinantes sobre su hogar.\\n\\n\"隆Mira! Hay una cueva escondida detr谩s de ese 谩rbol\" - exclam贸 Max emocionado.\\n\\n\"隆Vamos a investigarla!\" - anim贸 el b煤ho.\\n\\nYa no se sent铆a solo en su madriguera, sab铆a que ten铆a un amigo en quien pod铆a confiar. Y as铆, acurrucados en su madriguera, Max y el b煤ho se quedaron dormidos bajo la lluvia de estrellas, con la seguridad de que su amistad los llevar铆a a muchas m谩s aventuras y descubrimientos en el bosque m谩gico.'}\n"
]
}
],
"source": [
"from datasets import Dataset\n",
"count = -1\n",
"conversations = []\n",
"for item in full_dataset:\n",
" parent_id = item['parent_id']\n",
" text = item['text']\n",
" role = item['role']\n",
" \n",
" if parent_id is None:\n",
" conversations.append('')\n",
" count += 1\n",
" \n",
" \n",
" if role == 'prompter':\n",
" conversations[count] += '### Human: %s' % text\n",
" elif role == 'assistant':\n",
" conversations[count] += '### Assistant: %s' % text\n",
" \n",
"#convo_list = [c for c in conversations]\n",
"dataset = Dataset.from_dict(dict(text=conversations))\n",
"print(dataset[345])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-21T18:28:24.234945Z",
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},
"outputs": [],
"source": [
"from transformers import TrainingArguments\n",
"\n",
"output_dir = \"./results\"\n",
"per_device_train_batch_size = 2\n",
"gradient_accumulation_steps = 8\n",
"optim = \"paged_adamw_32bit\"\n",
"save_steps = 100\n",
"logging_steps = 10\n",
"learning_rate = 2e-4\n",
"max_grad_norm = 0.3\n",
"max_steps = 500\n",
"warmup_ratio = 0.03\n",
"lr_scheduler_type = \"constant\"\n",
"\n",
"training_arguments = TrainingArguments(\n",
" output_dir=output_dir,\n",
" per_device_train_batch_size=per_device_train_batch_size,\n",
" gradient_accumulation_steps=gradient_accumulation_steps,\n",
" optim=optim,\n",
" save_steps=save_steps,\n",
" logging_steps=logging_steps,\n",
" learning_rate=learning_rate,\n",
" fp16=True,\n",
" max_grad_norm=max_grad_norm,\n",
" max_steps=max_steps,\n",
" warmup_ratio=warmup_ratio,\n",
" group_by_length=True,\n",
" lr_scheduler_type=lr_scheduler_type\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-21T18:28:28.994021Z",
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.10/site-packages/trl/trainer/ppo_config.py:141: UserWarning: The `optimize_cuda_cache` arguement will be deprecated soon, please use `optimize_device_cache` instead.\n",
" warnings.warn(\n"
]
},
{
"data": {
"text/plain": [
"Map: 0%| | 0/9846 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from trl import SFTTrainer\n",
"\n",
"max_seq_length = 256\n",
"\n",
"trainer = SFTTrainer(\n",
" model=model,\n",
" train_dataset=dataset,\n",
" peft_config=peft_config,\n",
" dataset_text_field=\"text\",\n",
" max_seq_length=max_seq_length,\n",
" tokenizer=tokenizer,\n",
" args=training_arguments,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-21T18:29:19.034538Z",
"iopub.status.busy": "2023-12-21T18:29:19.034143Z",
"iopub.status.idle": "2023-12-21T18:29:19.045041Z",
"shell.execute_reply": "2023-12-21T18:29:19.043887Z",
"shell.execute_reply.started": "2023-12-21T18:29:19.034509Z"
}
},
"outputs": [],
"source": [
"for name, module in trainer.model.named_modules():\n",
" if \"norm\" in name:\n",
" module = module.to(torch.float32)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-21T18:29:28.833966Z",
"iopub.status.busy": "2023-12-21T18:29:28.833572Z",
"iopub.status.idle": "2023-12-21T19:54:14.245160Z",
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},
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{
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"\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
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"text": [
" 路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路路\n"
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"text": [
"\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
]
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{
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"text": [
"You're using a CodeGenTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
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" \n",
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" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
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" <td>170</td>\n",
" <td>1.678500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>180</td>\n",
" <td>1.643800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>190</td>\n",
" <td>1.758200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>1.767500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>210</td>\n",
" <td>1.735000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>220</td>\n",
" <td>1.635400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>230</td>\n",
" <td>1.666700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>240</td>\n",
" <td>1.690100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>250</td>\n",
" <td>1.763400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>260</td>\n",
" <td>1.700900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>270</td>\n",
" <td>1.606000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>280</td>\n",
" <td>1.679100</td>\n",
" </tr>\n",
" <tr>\n",
" <td>290</td>\n",
" <td>1.631000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>300</td>\n",
" <td>1.884500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>310</td>\n",
" <td>1.691000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>320</td>\n",
" <td>1.705400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>330</td>\n",
" <td>1.643000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>340</td>\n",
" <td>1.738400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>350</td>\n",
" <td>1.676200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>360</td>\n",
" <td>1.674900</td>\n",
" </tr>\n",
" <tr>\n",
" <td>370</td>\n",
" <td>1.719000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>380</td>\n",
" <td>1.614800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>390</td>\n",
" <td>1.648600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>400</td>\n",
" <td>1.857600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>410</td>\n",
" <td>1.743500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>420</td>\n",
" <td>1.696600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>430</td>\n",
" <td>1.621600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>440</td>\n",
" <td>1.643400</td>\n",
" </tr>\n",
" <tr>\n",
" <td>450</td>\n",
" <td>1.760000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>460</td>\n",
" <td>1.601600</td>\n",
" </tr>\n",
" <tr>\n",
" <td>470</td>\n",
" <td>1.605700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>480</td>\n",
" <td>1.664800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>490</td>\n",
" <td>1.646200</td>\n",
" </tr>\n",
" <tr>\n",
" <td>500</td>\n",
" <td>1.764400</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"TrainOutput(global_step=500, training_loss=1.7096557769775391, metrics={'train_runtime': 5081.3153, 'train_samples_per_second': 1.574, 'train_steps_per_second': 0.098, 'total_flos': 3.293667738832896e+16, 'train_loss': 1.7096557769775391, 'epoch': 0.81})"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-21T19:54:58.501371Z",
"iopub.status.busy": "2023-12-21T19:54:58.501013Z",
"iopub.status.idle": "2023-12-21T19:55:00.218026Z",
"shell.execute_reply": "2023-12-21T19:55:00.216901Z",
"shell.execute_reply.started": "2023-12-21T19:54:58.501343Z"
}
},
"outputs": [],
"source": [
"from transformers import (\n",
" AutoModelForCausalLM,\n",
" AutoTokenizer,\n",
" BitsAndBytesConfig,\n",
" HfArgumentParser,\n",
" TrainingArguments,\n",
" pipeline,\n",
" logging,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-21T20:08:21.028778Z",
"iopub.status.busy": "2023-12-21T20:08:21.027885Z",
"iopub.status.idle": "2023-12-21T20:08:36.121203Z",
"shell.execute_reply": "2023-12-21T20:08:36.120260Z",
"shell.execute_reply.started": "2023-12-21T20:08:21.028747Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"### Human: Explain blockchain to a five year old in two sentences.\n",
"\n",
"### Assistant: \n",
"Blockchain is like a big book where everyone can write down what they do, and then everyone can see it. It's like a big game of telephone, but instead of just one person telling the story, everyone can see it all at once.### Assistant: Blockchain is like a big book where everyone can write down what they do, and then everyone can see it. It's like a big game of telephone, but instead of just one person telling the story, everyone can see it all at once.### Assistant: Blockchain is like a big book where everyone can write down what they do, and then everyone can see it. It's like a big game of telephone, but instead of just one person telling the story, everyone can see it all at once.### Assistant: Blockchain is like a big book where everyone can write down what they do, and then everyone can see it.\n"
]
}
],
"source": [
"# Run text generation pipeline with our next model\n",
"prompt = \"Explain blockchain to a five year old in two sentences.\"\n",
"pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n",
"result = pipe(f\"### Human: {prompt}\\n\\n### Assistant: \")\n",
"print(result[0]['generated_text'])"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-21T20:47:38.133788Z",
"iopub.status.busy": "2023-12-21T20:47:38.132720Z",
"iopub.status.idle": "2023-12-21T20:47:40.133261Z",
"shell.execute_reply": "2023-12-21T20:47:40.132074Z",
"shell.execute_reply.started": "2023-12-21T20:47:38.133744Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
"To disable this warning, you can either:\n",
"\t- Avoid using `tokenizers` before the fork if possible\n",
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" adding: kaggle/working/results/runs/ (stored 0%)\n",
" adding: kaggle/working/results/runs/Dec21_18-28-24_6ea8eab21180/ (stored 0%)\n",
" adding: kaggle/working/results/runs/Dec21_18-28-24_6ea8eab21180/events.out.tfevents.1703183372.6ea8eab21180.42.0 (deflated 65%)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
"To disable this warning, you can either:\n",
"\t- Avoid using `tokenizers` before the fork if possible\n",
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"checkpoint-100\tcheckpoint-300\tcheckpoint-500\n",
"checkpoint-200\tcheckpoint-400\truns\n"
]
}
],
"source": [
"!zip -r runs.zip /kaggle/working/results/runs\n",
"!ls results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
"#model_path = \"/piyushgrover/phi-2-qlora-merged-custom\" # change to your preferred path\n",
"model_name = \"microsoft/phi-2\"\n",
"# device_map = {\"\": 1}\n",
"\n",
"# Reload model in FP16 and merge it with LoRA weights\n",
"base_model = AutoModelForCausalLM.from_pretrained(\n",
" model_name,\n",
" low_cpu_mem_usage=True,\n",
" return_dict=True,\n",
" torch_dtype=torch.float16,\n",
" trust_remote_code=True\n",
" # device_map=device_map,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from peft import PeftModel\n",
"new_model = \"/piyushgrover/phi-2-qlora-adapter-custom\"\n",
"model = PeftModel.from_pretrained(base_model, new_model)\n",
"model = model.merge_and_unload()\n",
"\n",
"# Reload tokenizer to save it\n",
"tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
"tokenizer.pad_token = tokenizer.eos_token\n",
"tokenizer.padding_side = \"right\"\n",
"\n",
"# Save the merged model\n",
"#model.save_pretrained(model_path)\n",
"#tokenizer.save_pretrained(model_path)\n",
"#from transformers import AutoModelForCausalLM, AutoTokenizer\n",
"\n",
"#model_path = \"/piyushgrover/phi-2-qlora-merged\" # change to your preferred path\n",
"\n",
"#model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)\n",
"#tokenizer = AutoTokenizer.from_pretrained(model_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from transformers import pipeline\n",
"\n",
"prompt = \"What was the role of indian revolutionaries in indian independence movement ?\" # change to your desired prompt\n",
"gen = pipeline('text-generation', model=model, tokenizer=tokenizer, max_length=500)\n",
"result = gen(prompt)\n",
"print(result[0]['generated_text'])"
]
}
],
"metadata": {
"kaggle": {
"accelerator": "gpu",
"dataSources": [],
"dockerImageVersionId": 30627.0,
"isGpuEnabled": true,
"isInternetEnabled": true,
"language": "python",
"sourceType": "notebook"
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|