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Alt weight loading code via PyTorch (#585)
* Alt weight loading code via PyTorch * commit additional files
This commit is contained in:
Sebastian Raschka
GitHub
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@ -113,7 +113,7 @@ Several folders contain optional materials as a bonus for interested readers:
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- **Chapter 4: Implementing a GPT model from scratch**
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- [FLOPS Analysis](ch04/02_performance-analysis/flops-analysis.ipynb)
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- **Chapter 5: Pretraining on unlabeled data:**
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- [Alternative Weight Loading from Hugging Face Model Hub using Transformers](ch05/02_alternative_weight_loading/weight-loading-hf-transformers.ipynb)
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- [Alternative Weight Loading Methods](ch05/02_alternative_weight_loading/)
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- [Pretraining GPT on the Project Gutenberg Dataset](ch05/03_bonus_pretraining_on_gutenberg)
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- [Adding Bells and Whistles to the Training Loop](ch05/04_learning_rate_schedulers)
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- [Optimizing Hyperparameters for Pretraining](ch05/05_bonus_hparam_tuning)
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@ -2133,20 +2133,53 @@
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"id": "127ddbdb-3878-4669-9a39-d231fbdfb834",
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"metadata": {},
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"source": [
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"<span style=\"color:darkred\">\n",
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" <ul>\n",
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" <li>For an alternative way to load the weights from the Hugging Face Hub, see <a href=\"../02_alternative_weight_loading\">../02_alternative_weight_loading</a></li>\n",
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" <ul>\n",
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" <li>This is useful if:</li>\n",
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" <ul>\n",
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" <li>the weights are temporarily unavailable</li>\n",
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" <li>a company VPN only permits downloads from the Hugging Face Hub but not from the OpenAI CDN, for example</li>\n",
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" <li>you are having trouble with the TensorFlow installation (the original weights are stored in TensorFlow files)</li>\n",
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" </ul>\n",
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" </ul>\n",
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" <li>The <a href=\"../02_alternative_weight_loading\">../02_alternative_weight_loading</a> code notebooks are replacements for the remainder of this section 5.5</li>\n",
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" </ul>\n",
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"</span>\n"
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"---\n",
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"\n",
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"---\n",
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"\n",
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"\n",
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"⚠️ **Note: Some users may encounter issues in this section due to TensorFlow compatibility problems, particularly on certain Windows systems. TensorFlow is required here only to load the original OpenAI GPT-2 weight files, which we then convert to PyTorch.\n",
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"If you're running into TensorFlow-related issues, you can use the alternative code below instead of the remaining code in this section.\n",
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"This alternative is based on pre-converted PyTorch weights, created using the same conversion process described in the previous section. For details, refer to the notebook:\n",
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"[../02_alternative_weight_loading/weight-loading-pytorch.ipynb](../02_alternative_weight_loading/weight-loading-pytorch.ipynb) notebook.**\n",
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"\n",
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"```python\n",
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"file_name = \"gpt2-small-124M.pth\"\n",
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"# file_name = \"gpt2-medium-355M.pth\"\n",
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"# file_name = \"gpt2-large-774M.pth\"\n",
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"# file_name = \"gpt2-xl-1558M.pth\"\n",
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"\n",
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"url = f\"https://huggingface.co/rasbt/gpt2-from-scratch-pytorch/resolve/main/{file_name}\"\n",
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"\n",
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"if not os.path.exists(file_name):\n",
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" urllib.request.urlretrieve(url, file_name)\n",
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" print(f\"Downloaded to {file_name}\")\n",
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"\n",
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"gpt = GPTModel(BASE_CONFIG)\n",
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"gpt.load_state_dict(torch.load(file_name, weights_only=True))\n",
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"gpt.eval()\n",
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"\n",
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"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
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"gpt.to(device);\n",
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"\n",
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"\n",
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"torch.manual_seed(123)\n",
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"\n",
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"token_ids = generate(\n",
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" model=gpt,\n",
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" idx=text_to_token_ids(\"Every effort moves you\", tokenizer).to(device),\n",
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" max_new_tokens=25,\n",
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" context_size=NEW_CONFIG[\"context_length\"],\n",
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" top_k=50,\n",
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" temperature=1.5\n",
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")\n",
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"\n",
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"print(\"Output text:\\n\", token_ids_to_text(token_ids, tokenizer))\n",
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"```\n",
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"\n",
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"---\n",
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"\n",
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"---"
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]
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},
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{
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@ -2197,7 +2230,10 @@
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"outputs": [],
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"source": [
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"# Relative import from the gpt_download.py contained in this folder\n",
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"from gpt_download import download_and_load_gpt2"
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"\n",
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"from gpt_download import download_and_load_gpt2\n",
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"# Alternatively:\n",
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"# from llms_from_scratch.ch05 import download_and_load_gpt2"
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]
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},
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{
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@ -2,6 +2,8 @@
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This folder contains alternative weight loading strategies in case the weights become unavailable from OpenAI.
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- [weight-loading-pytorch.ipynb](weight-loading-pytorch.ipynb): (Recommended) contains code to load the weights from PyTorch state dicts that I created by converting the original TensorFlow weights
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- [weight-loading-hf-transformers.ipynb](weight-loading-hf-transformers.ipynb): contains code to load the weights from the Hugging Face Model Hub via the `transformers` library
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- [weight-loading-hf-safetensors.ipynb](weight-loading-hf-safetensors.ipynb): contains code to load the weights from the Hugging Face Model Hub via the `safetensors` library directly (skipping the instantiation of a Hugging Face transformer model)
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356
ch05/02_alternative_weight_loading/weight-loading-pytorch.ipynb
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356
ch05/02_alternative_weight_loading/weight-loading-pytorch.ipynb
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@ -0,0 +1,356 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "6d6bc54f-2b16-4b0f-be69-957eed5d112f",
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"metadata": {},
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"source": [
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"<table style=\"width:100%\">\n",
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"<tr>\n",
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"<td style=\"vertical-align:middle; text-align:left;\">\n",
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"<font size=\"2\">\n",
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"Supplementary code for the <a href=\"http://mng.bz/orYv\">Build a Large Language Model From Scratch</a> book by <a href=\"https://sebastianraschka.com\">Sebastian Raschka</a><br>\n",
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"<br>Code repository: <a href=\"https://github.com/rasbt/LLMs-from-scratch\">https://github.com/rasbt/LLMs-from-scratch</a>\n",
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"</font>\n",
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"</td>\n",
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"<td style=\"vertical-align:middle; text-align:left;\">\n",
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"<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>\n",
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"</td>\n",
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"</tr>\n",
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"</table>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "72953590-5363-4398-85ce-54bde07f3d8a",
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"metadata": {},
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"source": [
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"# Bonus Code for Chapter 5"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1a4ab5ee-e7b9-45d3-a82b-a12bcfc0945a",
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"metadata": {},
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"source": [
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"## Alternative Weight Loading from PyTorch state dicts"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b2feea87-49f0-48b9-b925-b8f0dda4096f",
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"metadata": {},
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"source": [
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"- In the main chapter, we loaded the GPT model weights directly from OpenAI\n",
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"- This notebook provides alternative weight loading code to load the model weights from PyTorch state dict files that I created from the original TensorFlow files and uploaded to the [Hugging Face Model Hub](https://huggingface.co/docs/hub/en/models-the-hub) at [https://huggingface.co/rasbt/gpt2-from-scratch-pytorch](https://huggingface.co/rasbt/gpt2-from-scratch-pytorch)\n",
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"- This is conceptually the same as loading weights of a PyTorch model from via the state-dict method described in chapter 5:\n",
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"\n",
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"```python\n",
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"state_dict = torch.load(\"model_state_dict.pth\")\n",
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"model.load_state_dict(state_dict) \n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e3f9fbb2-3e39-41ee-8a08-58ba0434a8f3",
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"metadata": {},
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"source": [
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"### Choose model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "b0467eff-b43c-4a38-93e8-5ed87a5fc2b1",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"torch version: 2.6.0\n"
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]
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}
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],
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"source": [
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"from importlib.metadata import version\n",
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"\n",
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"pkgs = [\"torch\"]\n",
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"for p in pkgs:\n",
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" print(f\"{p} version: {version(p)}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "9ea9b1bc-7881-46ad-9555-27a9cf23faa7",
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"metadata": {},
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"outputs": [],
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"source": [
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"BASE_CONFIG = {\n",
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" \"vocab_size\": 50257, # Vocabulary size\n",
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" \"context_length\": 1024, # Context length\n",
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" \"drop_rate\": 0.0, # Dropout rate\n",
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" \"qkv_bias\": True # Query-key-value bias\n",
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"}\n",
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"\n",
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"model_configs = {\n",
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" \"gpt2-small (124M)\": {\"emb_dim\": 768, \"n_layers\": 12, \"n_heads\": 12},\n",
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" \"gpt2-medium (355M)\": {\"emb_dim\": 1024, \"n_layers\": 24, \"n_heads\": 16},\n",
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" \"gpt2-large (774M)\": {\"emb_dim\": 1280, \"n_layers\": 36, \"n_heads\": 20},\n",
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" \"gpt2-xl (1558M)\": {\"emb_dim\": 1600, \"n_layers\": 48, \"n_heads\": 25},\n",
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"}\n",
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"\n",
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"\n",
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"CHOOSE_MODEL = \"gpt2-small (124M)\"\n",
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"BASE_CONFIG.update(model_configs[CHOOSE_MODEL])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d78fc2b0-ba27-4aff-8aa3-bc6e04fca69d",
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"metadata": {},
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"source": [
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"### Download file"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "ca224672-a0f7-4b39-9bc9-19ddde69487b",
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"metadata": {},
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"outputs": [],
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"source": [
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"file_name = \"gpt2-small-124M.pth\"\n",
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"# file_name = \"gpt2-medium-355M.pth\"\n",
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"# file_name = \"gpt2-large-774M.pth\"\n",
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"# file_name = \"gpt2-xl-1558M.pth\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "e7b22375-6fac-4e90-9063-daa4de86c778",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Downloaded to gpt2-small-124M.pth\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import urllib.request\n",
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"\n",
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"url = f\"https://huggingface.co/rasbt/gpt2-from-scratch-pytorch/resolve/main/{file_name}\"\n",
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"\n",
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"if not os.path.exists(file_name):\n",
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" urllib.request.urlretrieve(url, file_name)\n",
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" print(f\"Downloaded to {file_name}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e61f0990-74cf-4b6d-85e5-4c7d0554db32",
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"metadata": {},
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"source": [
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"### Load weights"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "cda44d37-92c0-4c19-a70a-15711513afce",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"from llms_from_scratch.ch04 import GPTModel\n",
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"# For llms_from_scratch installation instructions, see:\n",
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"# https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg\n",
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"\n",
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"\n",
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"gpt = GPTModel(BASE_CONFIG)\n",
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"gpt.load_state_dict(torch.load(file_name, weights_only=True))\n",
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"gpt.eval()\n",
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"\n",
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"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
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"gpt.to(device);"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e0297fc4-11dc-4093-922f-dcaf85a75344",
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"metadata": {},
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"source": [
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"### Generate text"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "4ddd0d51-3ade-4890-9bab-d63f141d095f",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Output text:\n",
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" Every effort moves forward, but it's not enough.\n",
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"\n",
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"\"I'm not going to sit here and say, 'I'm not going to do this,'\n"
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]
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}
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],
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"source": [
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"import tiktoken\n",
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"from llms_from_scratch.ch05 import generate, text_to_token_ids, token_ids_to_text\n",
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"\n",
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"\n",
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"torch.manual_seed(123)\n",
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"\n",
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"tokenizer = tiktoken.get_encoding(\"gpt2\")\n",
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"\n",
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"token_ids = generate(\n",
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" model=gpt.to(device),\n",
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" idx=text_to_token_ids(\"Every effort moves\", tokenizer).to(device),\n",
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" max_new_tokens=30,\n",
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" context_size=BASE_CONFIG[\"context_length\"],\n",
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" top_k=1,\n",
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" temperature=1.0\n",
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")\n",
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"\n",
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"print(\"Output text:\\n\", token_ids_to_text(token_ids, tokenizer))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "aa4a7912-ae51-4786-8ef4-42bd53682932",
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"metadata": {},
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"source": [
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"## Alternative safetensors file"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2f774001-9cda-4b1f-88c5-ef99786a612b",
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"metadata": {},
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"source": [
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"- In addition, the [https://huggingface.co/rasbt/gpt2-from-scratch-pytorch](https://huggingface.co/rasbt/gpt2-from-scratch-pytorch) repository contains so-called `.safetensors` versions of the state dicts\n",
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"- The appeal of `.safetensors` files lies in their secure design, as they only store tensor data and avoid the execution of potentially malicious code during loading\n",
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"- In newer versions of PyTorch (e.g., 2.0 and newer), a `weights_only=True` argument can be used with `torch.load` (e.g., `torch.load(\"model_state_dict.pth\", weights_only=True)`) to improve safety by skipping the execution of code and loading only the weights (this is now enabled by default in PyTorch 2.6 and newer); so in that case loading the weights from the state dict files should not be a concern (anymore)\n",
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"- However, the code block below briefly shows how to load the model from these `.safetensor` files"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "c0a4fd86-4119-4a94-ae5e-13fb60d198bc",
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"metadata": {},
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"outputs": [],
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"source": [
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"file_name = \"gpt2-small-124M.safetensors\"\n",
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"# file_name = \"gpt2-medium-355M.safetensors\"\n",
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"# file_name = \"gpt2-large-774M.safetensors\"\n",
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"# file_name = \"gpt2-xl-1558M.safetensors\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "20f96c2e-3469-47fb-bad3-e9173a1f1ba3",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Downloaded to gpt2-small-124M.safetensors\n"
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]
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}
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],
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"source": [
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"import os\n",
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"import urllib.request\n",
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"\n",
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"url = f\"https://huggingface.co/rasbt/gpt2-from-scratch-pytorch/resolve/main/{file_name}\"\n",
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"\n",
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"if not os.path.exists(file_name):\n",
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" urllib.request.urlretrieve(url, file_name)\n",
|
||||
" print(f\"Downloaded to {file_name}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "d16a69b3-9bb4-42f8-8e4f-cc62a1a1a083",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load file\n",
|
||||
"\n",
|
||||
"from safetensors.torch import load_file\n",
|
||||
"\n",
|
||||
"gpt = GPTModel(BASE_CONFIG)\n",
|
||||
"gpt.load_state_dict(load_file(file_name))\n",
|
||||
"gpt.eval();"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "352e57f7-8d82-4c12-900c-03e41bc9de58",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Output text:\n",
|
||||
" Every effort moves forward, but it's not enough.\n",
|
||||
"\n",
|
||||
"\"I'm not going to sit here and say, 'I'm not going to do this,'\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"token_ids = generate(\n",
|
||||
" model=gpt.to(device),\n",
|
||||
" idx=text_to_token_ids(\"Every effort moves\", tokenizer).to(device),\n",
|
||||
" max_new_tokens=30,\n",
|
||||
" context_size=BASE_CONFIG[\"context_length\"],\n",
|
||||
" top_k=1,\n",
|
||||
" temperature=1.0\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"print(\"Output text:\\n\", token_ids_to_text(token_ids, tokenizer))"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"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": "3.10.16"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -79,7 +79,8 @@ from llms_from_scratch.ch05 import (
|
||||
token_ids_to_text,
|
||||
calc_loss_batch,
|
||||
calc_loss_loader,
|
||||
plot_losses
|
||||
plot_losses,
|
||||
download_and_load_gpt2
|
||||
)
|
||||
|
||||
from llms_from_scratch.ch06 import (
|
||||
|
||||
@ -4,10 +4,16 @@
|
||||
# Code: https://github.com/rasbt/LLMs-from-scratch
|
||||
|
||||
from .ch04 import generate_text_simple
|
||||
|
||||
import json
|
||||
import os
|
||||
import urllib.request
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.ticker import MaxNLocator
|
||||
import torch
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
def generate(model, idx, max_new_tokens, context_size, temperature=0.0, top_k=None, eos_id=None):
|
||||
@ -231,3 +237,119 @@ def plot_losses(epochs_seen, tokens_seen, train_losses, val_losses):
|
||||
fig.tight_layout() # Adjust layout to make room
|
||||
plt.savefig("loss-plot.pdf")
|
||||
plt.show()
|
||||
|
||||
|
||||
def download_and_load_gpt2(model_size, models_dir):
|
||||
import tensorflow as tf
|
||||
|
||||
# Validate model size
|
||||
allowed_sizes = ("124M", "355M", "774M", "1558M")
|
||||
if model_size not in allowed_sizes:
|
||||
raise ValueError(f"Model size not in {allowed_sizes}")
|
||||
|
||||
# Define paths
|
||||
model_dir = os.path.join(models_dir, model_size)
|
||||
base_url = "https://openaipublic.blob.core.windows.net/gpt-2/models"
|
||||
backup_base_url = "https://f001.backblazeb2.com/file/LLMs-from-scratch/gpt2"
|
||||
filenames = [
|
||||
"checkpoint", "encoder.json", "hparams.json",
|
||||
"model.ckpt.data-00000-of-00001", "model.ckpt.index",
|
||||
"model.ckpt.meta", "vocab.bpe"
|
||||
]
|
||||
|
||||
# Download files
|
||||
os.makedirs(model_dir, exist_ok=True)
|
||||
for filename in filenames:
|
||||
file_url = os.path.join(base_url, model_size, filename)
|
||||
backup_url = os.path.join(backup_base_url, model_size, filename)
|
||||
file_path = os.path.join(model_dir, filename)
|
||||
download_file(file_url, file_path, backup_url)
|
||||
|
||||
# Load settings and params
|
||||
tf_ckpt_path = tf.train.latest_checkpoint(model_dir)
|
||||
settings = json.load(open(os.path.join(model_dir, "hparams.json"), "r", encoding="utf-8"))
|
||||
params = load_gpt2_params_from_tf_ckpt(tf_ckpt_path, settings)
|
||||
|
||||
return settings, params
|
||||
|
||||
|
||||
def download_file(url, destination, backup_url=None):
|
||||
def _attempt_download(download_url):
|
||||
with urllib.request.urlopen(download_url) as response:
|
||||
# Get the total file size from headers, defaulting to 0 if not present
|
||||
file_size = int(response.headers.get("Content-Length", 0))
|
||||
|
||||
# Check if file exists and has the same size
|
||||
if os.path.exists(destination):
|
||||
file_size_local = os.path.getsize(destination)
|
||||
if file_size == file_size_local:
|
||||
print(f"File already exists and is up-to-date: {destination}")
|
||||
return True # Indicate success without re-downloading
|
||||
|
||||
block_size = 1024 # 1 Kilobyte
|
||||
|
||||
# Initialize the progress bar with total file size
|
||||
progress_bar_description = os.path.basename(download_url)
|
||||
with tqdm(total=file_size, unit="iB", unit_scale=True, desc=progress_bar_description) as progress_bar:
|
||||
with open(destination, "wb") as file:
|
||||
while True:
|
||||
chunk = response.read(block_size)
|
||||
if not chunk:
|
||||
break
|
||||
file.write(chunk)
|
||||
progress_bar.update(len(chunk))
|
||||
return True
|
||||
|
||||
try:
|
||||
if _attempt_download(url):
|
||||
return
|
||||
except (urllib.error.HTTPError, urllib.error.URLError):
|
||||
if backup_url is not None:
|
||||
print(f"Primary URL ({url}) failed. Attempting backup URL: {backup_url}")
|
||||
try:
|
||||
if _attempt_download(backup_url):
|
||||
return
|
||||
except urllib.error.HTTPError:
|
||||
pass
|
||||
|
||||
# If we reach here, both attempts have failed
|
||||
error_message = (
|
||||
f"Failed to download from both primary URL ({url})"
|
||||
f"{' and backup URL (' + backup_url + ')' if backup_url else ''}."
|
||||
"\nCheck your internet connection or the file availability.\n"
|
||||
"For help, visit: https://github.com/rasbt/LLMs-from-scratch/discussions/273"
|
||||
)
|
||||
print(error_message)
|
||||
except Exception as e:
|
||||
print(f"An unexpected error occurred: {e}")
|
||||
|
||||
|
||||
def load_gpt2_params_from_tf_ckpt(ckpt_path, settings):
|
||||
import tensorflow as tf
|
||||
|
||||
# Initialize parameters dictionary with empty blocks for each layer
|
||||
params = {"blocks": [{} for _ in range(settings["n_layer"])]}
|
||||
|
||||
# Iterate over each variable in the checkpoint
|
||||
for name, _ in tf.train.list_variables(ckpt_path):
|
||||
# Load the variable and remove singleton dimensions
|
||||
variable_array = np.squeeze(tf.train.load_variable(ckpt_path, name))
|
||||
|
||||
# Process the variable name to extract relevant parts
|
||||
variable_name_parts = name.split("/")[1:] # Skip the 'model/' prefix
|
||||
|
||||
# Identify the target dictionary for the variable
|
||||
target_dict = params
|
||||
if variable_name_parts[0].startswith("h"):
|
||||
layer_number = int(variable_name_parts[0][1:])
|
||||
target_dict = params["blocks"][layer_number]
|
||||
|
||||
# Recursively access or create nested dictionaries
|
||||
for key in variable_name_parts[1:-1]:
|
||||
target_dict = target_dict.setdefault(key, {})
|
||||
|
||||
# Assign the variable array to the last key
|
||||
last_key = variable_name_parts[-1]
|
||||
target_dict[last_key] = variable_array
|
||||
|
||||
return params
|
||||
|
||||
@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "llms-from-scratch"
|
||||
version = "1.0.1"
|
||||
version = "1.0.2"
|
||||
description = "Implement a ChatGPT-like LLM in PyTorch from scratch, step by step"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10"
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user