From d2962f65cb70924262c43f54a19b75339db3b426 Mon Sep 17 00:00:00 2001 From: rasbt Date: Tue, 19 Mar 2024 16:34:34 -0500 Subject: [PATCH 01/13] Zero to LitGPT --- tutorials/0_to_litgpt.md | 219 ++++++++++++++++++++ tutorials/images/0_to_litgpt/4-commands.png | Bin 0 -> 68510 bytes tutorials/images/0_to_litgpt/finetune.png | Bin 0 -> 61736 bytes tutorials/images/0_to_litgpt/pretrain.png | Bin 0 -> 56186 bytes tutorials/images/0_to_litgpt/usage.png | Bin 0 -> 147536 bytes 5 files changed, 219 insertions(+) create mode 100644 tutorials/0_to_litgpt.md create mode 100644 tutorials/images/0_to_litgpt/4-commands.png create mode 100644 tutorials/images/0_to_litgpt/finetune.png create mode 100644 tutorials/images/0_to_litgpt/pretrain.png create mode 100644 tutorials/images/0_to_litgpt/usage.png diff --git a/tutorials/0_to_litgpt.md b/tutorials/0_to_litgpt.md new file mode 100644 index 0000000000..0dfdfd5992 --- /dev/null +++ b/tutorials/0_to_litgpt.md @@ -0,0 +1,219 @@ +# Zero to Pretraining, Finetuning, and Using LLMs with LitGPT + + + +This tutorial walks you through the main features and usage patterns for the LitGPT, a library for pretraining, finetuning, and using LLMs that focuses on an efficient user experience while being developer friendly. + +The topics, following the installation of LitGPT, are in chronological order reflecting the steps in an LLM lifecyle: Pretraining → Finetuning → Inference. + +  + + + +  + + + +  + +However, it is also possible, and even common, to use and deploy models with LitGPT without pretraining and finetuning. So, if you are not interested in pretraining and finetuning, please feel free to skip these sections. + + + + + +  +## Install LitGPT + +LitGPT is available as a Python library from the PyPI package repository, and we recommend installing it using Python's `pip` installer module, including all required package dependencies: + +```bash +pip install 'litgpt[all]' +``` + +Alternatively, if you are a researcher or developer planning to make changes to LitGPT, you can clone the GitHub repository and install it from a local folder as follows: + +``` +git clone https://github.com/Lightning-AI/litgpt.git +cd litgpt +pip install -e '.[all]' +``` + + +  +## Pretrain LLMs + +Finetuning LLMs requires substantial compute resources and time commitment. For that reason, most researchers and practitioners prefer to skip step and prefer to skip this step, downloading a pretrained LLM instead. + +However, if you are feeling adventerous and want to pretrain your own LLM, here's how. + +First, we have to decide which type of model architecture we want to use. We list the available architectures by using the `pretrain` command without any additional arguments: + +```bash +litgpt pretrain +``` + +This prints a list of all available model architectures in alphabetical order: + +``` +Camel-Platypus2-13B +Camel-Platypus2-70B +CodeLlama-13b-Python-hf +... +tiny-llama-1.1b +vicuna-13b-v1.3 +vicuna-13b-v1.5 +vicuna-13b-v1.5-16k +vicuna-33b-v1.3 +vicuna-7b-v1.3 +vicuna-7b-v1.5 +vicuna-7b-v1.5-16k +``` + +Suppose we are interested in pretraining 1.1B parameter small `tiny-llama-1.1b` model. Before we can start finetuning, we also need to choose an download a tokenizer. We can do this via the `download` command. Note that running `litgpt download` without any additional arguments will also print a list of all available models and tokenizers to download. To filter for specific models, e.g., TinyLlama, we can use the `grep` command in our terminal: + +```bash +litgpt download | grep TinyLlama +``` + +This prints + +``` +TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T +TinyLlama/TinyLlama-1.1B-Chat-v1.0 +``` + +Let's now download the tokenizer corresponding to `TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T` that we can then use to pretrain the TinyLlama model, which save the download tokenizer to a `checkpoints/` folder by default: + +``` +litgpt download \ + --repo_id TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T \ + --tokenizer_only true +``` + +  + + + +  + +Next, we can pretrain the model on the OpenWebText dataset with default setting as follows: + +```bash +litgpt pretrain \ + --model_name tiny-llama-1.1b \ + --data OpenWebText \ + --tokenizer_dir checkpoints/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T +``` + +If you are interested in additional settings, you can use the help command as follows: + +``` +litgpt pretrain --help +``` + +  + +> [!TIP] +> Above, we only covered the most basic commands for pretraining a model using LitGPT. If you are interested in pretraining a model, we highly recommend checking the resources below. + +  + +**More information and additional resources** + +- [tutorials/pretrain_tinyllama](./pretrain_tinyllama.md): A tutorials for finetuning a 1.1B TinyLlama model on 3 trillion tokens +- Project templates in reproducible environments with multi-GPU and multi-node support: + - [Prepare the TinyLlama 1T token dataset](https://lightning.ai/lightning-ai/studios/prepare-the-tinyllama-1t-token-dataset) + - [Pretrain LLMs - TinyLlama 1.1B](https://lightning.ai/lightning-ai/studios/pretrain-llms-tinyllama-1-1b) + - [Continued Pretraining with TinyLlama 1.1B](https://lightning.ai/lightning-ai/studios/continued-pretraining-with-tinyllama-1-1b) + + +  +## Download pretrained model weights + +Most practical usecases, like inference (/chat) or finetuning with LLMs, involve pretrained model weights. LitGPT supports a large number of model weights, which can be listed by executing the `download` command without any additional arguments: + +```bash +litgpt download +``` + +This will print a (long) list of all supported pretrained models (abbreviated for readability below): + +``` +.. +google/gemma-2b +... +meta-llama/Llama-2-7b-hf +... +microsoft/phi-2 +... +mistralai/Mixtral-8x7B-Instruct-v0.1 +... +``` + +To download the model weights, simply provide one of the model strings above as a `--repo_id` argument: + +```bash +litgpt download --repo_id microsoft/phi-2 +``` + +By default, the weights are going to be stored in a `./checkpoints` subdirectory: + +```bash +ls -lh checkpoints/microsoft/phi-2/ +``` + +``` +total 11G +-rw-r--r-- 1 sebastian sebastian 863 Mar 19 21:14 config.json +-rw-r--r-- 1 sebastian sebastian 124 Mar 19 21:14 generation_config.json +-rw-r--r-- 1 sebastian sebastian 5.2G Mar 19 21:15 lit_model.pth +-rw-r--r-- 1 sebastian sebastian 4.7G Mar 19 21:15 model-00001-of-00002.bin +-rw-r--r-- 1 sebastian sebastian 538M Mar 19 21:15 model-00002-of-00002.bin +-rw-r--r-- 1 sebastian sebastian 528 Mar 19 21:15 model_config.yaml +-rw-r--r-- 1 sebastian sebastian 2.1M Mar 19 21:14 tokenizer.json +-rw-r--r-- 1 sebastian sebastian 7.2K Mar 19 21:14 tokenizer_config.json +``` + +The model is now ready for inference and chat, for example, using the `chat` command on the checkpoint directory: + +```bash +litgpt chat --checkpoint_dir checkpoints/microsoft/phi-2 +``` + +``` +Now chatting with phi-2. +To exit, press 'Enter' on an empty prompt. + +Seed set to 1234 +>> Prompt: Why are LLMs so useful? +>> Reply: When building applications or operating systems, you can use LLMs to know how a computer should respond to your commands. This can make your programs run faster and more efficiently. + +Time for inference: 1.26 sec total, 27.81 tokens/sec, 35 tokens + +>> Prompt: +``` + + + +**More information** + +  +## Finetune LLMs + +  + + + +  + +- multiple GPUs +- Config files +- mix and match + +**More information** + +  +## Inference (/chat) + +**More information** \ No newline at end of file diff --git a/tutorials/images/0_to_litgpt/4-commands.png b/tutorials/images/0_to_litgpt/4-commands.png new file mode 100644 index 0000000000000000000000000000000000000000..f2ea142079590d0566ab2a67298f7f7d87e83476 GIT binary patch literal 68510 zcmeFYgB*7uLLvWX16By)M zdC%VGoO{3j;68hvXHEBNU%#&I>guYBc&jRhgGG)70)cQ8J$WluxcSvQg0Qcq`+@o94#Ss79fy(L~<&+CgDCw&||s+Qyv;^_RmakHb_I#8i}Yd zBsvZigQ1TI^=M!Ns~U|MpN+K*Ip4G(oC=$ShB+LrrIEJK1x{$lQ-&fbr(e0t z9yw@EkgS#0ny8|#PT`LgrN93WaDd*T4h@Q63Se(>c)W1Fa~vE>QrY2> zBNbGkq>Qj>;MjX3>romUItcA$()&gMr`hGza~h#{WvJ{^*m1s-BKGC63R7{31jki{ z(^<%=ahnj}@eOVq$;(s|y^0bXr2MUEtS9@WC+CM1>&9-0Ouvdt4A|qEUWq$jvcyjp 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Adapter and LoRA (short for low-rank adaptation) are methods for parameter-efficient finetuning that only require updating a small fraction of the model weights. +     -- multiple GPUs -- Config files -- mix and match +Parameter-efficient finetuning is much more resource-efficient and cheaper than full finetuning, and it often results in the same good performance on downstream tasks. + +In the following example, we will use LoRA for finetuning, which is one of the most popular LLM finetuning method. (For more information on how LoRA works, please see [Code LoRA from Scratch](https://lightning.ai/lightning-ai/studios/code-lora-from-scratch).) + +Before we start, we have to download a model as explained in the previous "Download pretrained model" section above: + +```bash +litgpt download --repo_id microsoft/phi-2 +``` + +The LitGPT interface can be used with via command line arguments and configuration files. We recommend starting out with the configuration files from the [config_hub](../config_hub) and either modifying them directly or overrriding certain settings via the command line. For example, we can use the following setting to train the downloaded 2.7B parameter `microsoft/phi-2` model, where we set `--max_steps 5` for a quick test run. + +If you have downloaded or cloned the LitGPT repository, you can provide the `config` file via a relative path: + +```bash +litgpt finetune lora \ + --config config_hub/finetune/phi-2/lora.yaml \ + --train.max_steps 5 +``` + +Alternatively, you can provide an URL: + +```bash +litgpt finetune lora \ + --config https://raw.githubusercontent.com/Lightning-AI/litgpt/main/config_hub/finetune/phi-2/lora.yaml \ + --train.max_steps 5 +``` + +  + + +> [!TIP] +> Note that the config file above will finetune the model on the `Alpaca2k` dataset on 1 GPU and save the resulting files in an `out/finetune/lora-phi-2` directory. All of these settings can be changed via a respective command line argument or by changing the config file. +> To see more options, execute `litgpt finetune lora --help`. + + + +  + +**More information and additional resources** + +- [tutorials/prepare_dataset](prepare_dataset) +- [tutorials/finetune](finetune.md) +- [tutorials/finetune_full](finetune_full.md) +- [tutorials/finetune_lora](finetune_lora.md) +- [tutorials/finetune_adapter](finetune_adapter.md) +- [tutorials/oom](oom.md) +- [tutorials/quantize](quantize.md) -**More information** +TODO: Mention config file resources   ## Inference (/chat) -**More information** \ No newline at end of file +  +**More information and additional resources** + +- [tutorials/inference](inference.md) + + +## Evaluation + +  +**More information and additional resources** + +- [tutorials/evaluation](evaluation.md) +- [tutorials/convert_lit_models](convert_lit_models.md) + + +--- + +TODOS: + +- [ ] Proofread +- [ ] Compress images to webp +- [ ] 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zv=)Bw1;(Y_7lj$C=by8y%ojA?npkk&T~CX#sDy>|ID1~;W8zX4@Lsw!uw<*-Xj8)x zNgS}?2+{f7>p+^lvU_8Bc8J^xDnlI#n|DLka%Upz@Yzke*UL9bEpOzp=U@DMuYrOd z$9|dOHu?67QrM*dFrLLtCb@cNQc=QoFow9=n( z+s!pHpoqN5@Ve|)vtkN?LnWArk`2$`Qx^6y1jykXmz$f6Wv0d$d zWob!aAD38dg*q+1I_QaYwpKO`-=5JUC3IQx91i*$@A*A=*~E87>RE*rLzN8rlEf~* z=RGD1h!R!!P6x;&1g~1W^y(pZINi4D?BXHJEnQot0Hawnzo$e4E_ZCTLDGxf|4(s{ zNC~Ka0T;l6sIgY^QAH;UEGXoN2vPOxmCOj?s6L07ncTP#Oen# z5@zgrJ+3x`j1eL5y^nqnZ!=sZ5bpKA0YCVX{ClS?u6Oix`Lyb5j=iroIvDdl49E(* zX2{$|?hB)~gUF}u;lc!U&~W}-3TJP-$SB8(0$k<2R|85D+xX11+!~3 z7w$j5b>WIcbb#+>cVF`C;#lk4eagbty#LQN{)i&~*^j^KkAL>#pZ)kpfBdN(|Iw$v zx?~_L{f~Y5OI-EOe*7aJ|IheR=+-cw-+sf8mVEwH`5!&-j~@8{8$ Date: Tue, 19 Mar 2024 18:59:26 -0500 Subject: [PATCH 03/13] update --- tutorials/0_to_litgpt.md | 124 ++++++++++++++++++++++++++++++++++++--- 1 file changed, 116 insertions(+), 8 deletions(-) diff --git a/tutorials/0_to_litgpt.md b/tutorials/0_to_litgpt.md index d5ca00dc20..00b456d1ed 100644 --- a/tutorials/0_to_litgpt.md +++ b/tutorials/0_to_litgpt.md @@ -249,9 +249,10 @@ Alternatively, you can provide an URL: ```bash litgpt finetune lora \ --config https://raw.githubusercontent.com/Lightning-AI/litgpt/main/config_hub/finetune/phi-2/lora.yaml \ - --train.max_steps 5 + --train.max_steps 5 ``` +   @@ -259,29 +260,136 @@ litgpt finetune lora \ > Note that the config file above will finetune the model on the `Alpaca2k` dataset on 1 GPU and save the resulting files in an `out/finetune/lora-phi-2` directory. All of these settings can be changed via a respective command line argument or by changing the config file. > To see more options, execute `litgpt finetune lora --help`. +  + +Running the previous finetuning command will initiate the finetuning process, which should only take about a minute on a GPU due to the `--train.max_steps 5` setting. + +``` +{'checkpoint_dir': PosixPath('checkpoints/microsoft/phi-2'), + 'data': Alpaca2k(mask_prompt=False, + val_split_fraction=0.03847, + prompt_style=, + ignore_index=-100, + seed=42, + num_workers=4, + download_dir=PosixPath('data/alpaca2k')), + 'devices': 1, + 'eval': EvalArgs(interval=100, max_new_tokens=100, max_iters=100), + 'logger_name': 'csv', + 'lora_alpha': 16, + 'lora_dropout': 0.05, + 'lora_head': True, + 'lora_key': True, + 'lora_mlp': True, + 'lora_projection': True, + 'lora_query': True, + 'lora_r': 8, + 'lora_value': True, + 'out_dir': PosixPath('out/finetune/lora-phi-2'), + 'precision': 'bf16-true', + 'quantize': None, + 'seed': 1337, + 'train': TrainArgs(save_interval=800, + log_interval=1, + global_batch_size=8, + micro_batch_size=4, + lr_warmup_steps=10, + epochs=1, + max_tokens=None, + max_steps=5, + max_seq_length=512, + tie_embeddings=None, + learning_rate=0.0002, + weight_decay=0.0, + beta1=0.9, + beta2=0.95, + max_norm=None, + min_lr=6e-05)} +Seed set to 1337 +Number of trainable parameters: 12,226,560 +Number of non trainable parameters: 2,779,683,840 +The longest sequence length in the train data is 512, the model's maximum sequence length is 512 and context length is 2048 +Validating ... +Recommend a movie for me to watch during the weekend and explain the reason. +Below is an instruction that describes a task. Write a response that appropriately completes the request. + +### Instruction: +Recommend a movie for me to watch during the weekend and explain the reason. + +### Response: +I recommend you watch "Parasite" because it's a critically acclaimed movie that won multiple awards, including the Academy Award for Best Picture. It's a thought-provoking and suspenseful film that will keep you on the edge of your seat. The movie also tackles social and economic inequalities, making it a must-watch for anyone interested in meaningful storytelling. + +/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/torchmetrics/utilities/prints.py:43: UserWarning: The ``compute`` method of metric MeanMetric was called before the ``update`` method which may lead to errors, as metric states have not yet been updated. + warnings.warn(*args, **kwargs) # noqa: B028 +Missing logger folder: out/finetune/lora-phi-2/logs/csv +Epoch 1 | iter 1 step 0 | loss train: 1.646, val: n/a | iter time: 820.31 ms +Epoch 1 | iter 2 step 1 | loss train: 1.660, val: n/a | iter time: 548.72 ms (step) +Epoch 1 | iter 3 step 1 | loss train: 1.687, val: n/a | iter time: 300.07 ms +Epoch 1 | iter 4 step 2 | loss train: 1.597, val: n/a | iter time: 595.27 ms (step) +Epoch 1 | iter 5 step 2 | loss train: 1.640, val: n/a | iter time: 260.75 ms +Epoch 1 | iter 6 step 3 | loss train: 1.703, val: n/a | iter time: 568.22 ms (step) +Epoch 1 | iter 7 step 3 | loss train: 1.678, val: n/a | iter time: 511.70 ms +Epoch 1 | iter 8 step 4 | loss train: 1.741, val: n/a | iter time: 514.14 ms (step) +Epoch 1 | iter 9 step 4 | loss train: 1.689, val: n/a | iter time: 423.59 ms +Epoch 1 | iter 10 step 5 | loss train: 1.524, val: n/a | iter time: 603.03 ms (step) +Training time: 11.20s +Memory used: 13.90 GB +Saving LoRA weights to 'out/finetune/lora-phi-2/final/lit_model.pth.lora' +Saved merged weights to 'out/finetune/lora-phi-2/final/lit_model.pth' +``` + +Notice that the LoRA script saves both the LoRA weights (`'out/finetune/lora-phi-2/final/lit_model.pth.lora'`) and the LoRA weight merged back into the original model (`'out/finetune/lora-phi-2/final/lit_model.pth'`) for convenience. This allows us to use the finetuned model via the `chat` function directly: + +```bash +litgpt chat --checkpoint_dir checkpoints/microsoft/phi-2 +``` + +``` +Now chatting with phi-2. +To exit, press 'Enter' on an empty prompt. + +Seed set to 1234 +>> Prompt: Why are LLMs so useful? +>> Reply: LLMs are useful because they can be trained to perform various natural language tasks, such as language translation, text generation, and question answering. They are also able to understand the context of the input data, which makes them particularly useful for tasks such as sentiment analysis and text summarization. Additionally, because LLMs can learn from large amounts of data, they are able to generalize well and perform well on new data. + +Time for inference: 2.15 sec total, 39.57 tokens/sec, 85 tokens + +>> Prompt: +``` +   **More information and additional resources** -- [tutorials/prepare_dataset](prepare_dataset) -- [tutorials/finetune](finetune.md) -- [tutorials/finetune_full](finetune_full.md) -- [tutorials/finetune_lora](finetune_lora.md) -- [tutorials/finetune_adapter](finetune_adapter.md) -- [tutorials/oom](oom.md) -- [tutorials/quantize](quantize.md) +- [tutorials/prepare_dataset](prepare_dataset): A summary of all out-of-the-box supported datasets in LitGPT and utilities for preparing custom datasets +- [tutorials/finetune](finetune.md): An overview of the different finetuning methods supported in LitGPT +- [tutorials/finetune_full](finetune_full.md): A tutorial on full-parameter finetuning +- [tutorials/finetune_lora](finetune_lora.md): Options for parameter-efficient finetuning with LoRA and QLoRA +- [tutorials/finetune_adapter](finetune_adapter.md): A description of the parameter-efficient Llama-Adapter methods supported in LitGPT +- [tutorials/oom](oom.md): Tips for dealing with out-of-memory (OOM) errors TODO: Mention config file resources   ## Inference (/chat) +To use a downloaded or finetuned model for chat, you only need to provide the corresponding checkpoint directory containing the model and tokenizer files. For example, to chat with the phi-2 model from Microsoft, download it as follows: + +```bash +litgpt download --repo_id microsoft/phi-2 +``` + +As described in the "Download pretrained model section", this f + + +   **More information and additional resources** - [tutorials/inference](inference.md) +- [tutorials/quantize](quantize.md): ## Evaluation From 82a4453f3fa53bc2cf196e608bdc39ab21b5ce51 Mon Sep 17 00:00:00 2001 From: rasbt Date: Tue, 19 Mar 2024 20:36:58 -0500 Subject: [PATCH 04/13] update --- tutorials/0_to_litgpt.md | 67 +++++++++++++++++++++++++++++++++++++--- 1 file changed, 62 insertions(+), 5 deletions(-) diff --git a/tutorials/0_to_litgpt.md b/tutorials/0_to_litgpt.md index 00b456d1ed..3c38948c16 100644 --- a/tutorials/0_to_litgpt.md +++ b/tutorials/0_to_litgpt.md @@ -157,6 +157,26 @@ To download the model weights, simply provide one of the model strings above as litgpt download --repo_id microsoft/phi-2 ``` +``` +model-00001-of-00002.safetensors: 100%|████████████████████████████████| 5.00G/5.00G [00:40<00:00, 124MB/s] +model-00002-of-00002.safetensors: 100%|████████████████████████████████| 564M/564M [00:01<00:00, 330MB/s] +tokenizer.json: 100%|██████████████████████████████████████████████████| 2.11M/2.11M [00:00<00:00, 54.0MB/s] +... +Converting checkpoint files to LitGPT format. +Processing checkpoints/microsoft/phi-2/model-00001-of-00002.bin +... +Saving converted checkpoint to checkpoints/microsoft/phi-2 +``` + + +  + +> [!TIP] +> Note that some models, such as Llama 2, require that you accept Meta AI's terms of service for this model, and you need to use a special access token via the `litgpt download ... --access_token ...` option. For more information, go to the respective Model Hub website, e.g., [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf). The access token can be created under your Model Hub in the Profile > Access Tokens menu. + +  + + By default, the weights are going to be stored in a `./checkpoints` subdirectory: ```bash @@ -341,7 +361,7 @@ Saved merged weights to 'out/finetune/lora-phi-2/final/lit_model.pth' Notice that the LoRA script saves both the LoRA weights (`'out/finetune/lora-phi-2/final/lit_model.pth.lora'`) and the LoRA weight merged back into the original model (`'out/finetune/lora-phi-2/final/lit_model.pth'`) for convenience. This allows us to use the finetuned model via the `chat` function directly: ```bash -litgpt chat --checkpoint_dir checkpoints/microsoft/phi-2 +litgpt chat --checkpoint_dir out/finetune/lora-phi-2/final/ ``` ``` @@ -375,25 +395,62 @@ TODO: Mention config file resources   ## Inference (/chat) -To use a downloaded or finetuned model for chat, you only need to provide the corresponding checkpoint directory containing the model and tokenizer files. For example, to chat with the phi-2 model from Microsoft, download it as follows: +To use a downloaded or finetuned model for chat, you only need to provide the corresponding checkpoint directory containing the model and tokenizer files. For example, to chat with the phi-2 model from Microsoft, download it as follows, as described in the "Download pretrained model" section: ```bash litgpt download --repo_id microsoft/phi-2 ``` -As described in the "Download pretrained model section", this f +``` +model-00001-of-00002.safetensors: 100%|████████████████████████████████| 5.00G/5.00G [00:40<00:00, 124MB/s] +model-00002-of-00002.safetensors: 100%|████████████████████████████████| 564M/564M [00:01<00:00, 330MB/s] +tokenizer.json: 100%|██████████████████████████████████████████████████| 2.11M/2.11M [00:00<00:00, 54.0MB/s] +... +Converting checkpoint files to LitGPT format. +Processing checkpoints/microsoft/phi-2/model-00001-of-00002.bin +... +Saving converted checkpoint to checkpoints/microsoft/phi-2 +``` + + +Then, chat with the model using + +```bash +litgpt chat --checkpoint_dir +``` + +``` +Now chatting with phi-2. +To exit, press 'Enter' on an empty prompt. + +Seed set to 1234 +>> Prompt: What is the main difference between a large language model and a traditional search engine? +>> Reply: A large language model uses deep learning algorithms to analyze and generate natural language, while a traditional search engine uses algorithms to retrieve information from webpages. + +Time for inference: 1.14 sec total, 26.26 tokens/sec, 30 tokens +``` + +> [!TIP] +> Most model weights are already represented in an efficient bfloat16 format. However, if the model currently exceeds your GPU memory, you can try to pass the `--precision bf16-true` option. In addition, you can check the quantization documentation for further optimization linked below. + +   **More information and additional resources** -- [tutorials/inference](inference.md) -- [tutorials/quantize](quantize.md): +- [tutorials/inference](inference.md): Chat and inference tutorial +- [tutorials/quantize](quantize.md): Quantizing models to reduce GPU memory requirements ## Evaluation +When you are deciding which model to use or evaluating finetuning results, it may be useful to find out how well a model is performing. For this purpose, you can use the integrated evaluation utility based on the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). + +> [!TIP] +> Use the `litgpt eval --help` command to see additional options. +   **More information and additional resources** From 9d8c0114dd37be1c2299e1a0dae619a9af7db75a Mon Sep 17 00:00:00 2001 From: rasbt Date: Wed, 20 Mar 2024 13:57:30 -0500 Subject: [PATCH 05/13] update --- tutorials/images/0_to_litgpt/4-commands.png | Bin 68510 -> 0 bytes tutorials/images/0_to_litgpt/4-commands.webp | Bin 0 -> 33466 bytes tutorials/images/0_to_litgpt/finetune.png | Bin 61736 -> 0 bytes tutorials/images/0_to_litgpt/finetune.webp | Bin 0 -> 25058 bytes tutorials/images/0_to_litgpt/instruction-1.png | Bin 218242 -> 0 bytes .../images/0_to_litgpt/instruction-1.webp | Bin 0 -> 84740 bytes tutorials/images/0_to_litgpt/instruction-2.png | Bin 132112 -> 0 bytes .../images/0_to_litgpt/instruction-2.webp | Bin 0 -> 47454 bytes 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6605e5f1e61dc9e05fcf272e3b577d2a704051c4 Mon Sep 17 00:00:00 2001 From: rasbt Date: Wed, 20 Mar 2024 13:57:51 -0500 Subject: [PATCH 06/13] add readme links --- README.md | 17 ++++- tutorials/0_to_litgpt.md | 158 +++++++++++++++++++++++++++++---------- 2 files changed, 134 insertions(+), 41 deletions(-) diff --git a/README.md b/README.md index 4023400cb7..b15f4e1e7e 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,6 @@ ✅  Readable and easy-to-modify code to experiment with the latest research ideas. -  
  @@ -59,8 +58,6 @@ The following [Lightning Studio](https://lightning.ai/lightning-ai/studios) temp - -  
  @@ -107,6 +104,14 @@ For more information, refer to the [download](tutorials/download_model_weights.m   + +> [!NOTE] +> We recommend starting with the **[Zero to Pretraining, Finetuning, and Using LLMs with LitGPT](https://chat.openai.com/c/tutorial/0_to_litgpt.md)** if you are looking to get started with using LitGPT. + + + +  + ## Finetuning and pretraining LitGPT supports [pretraining](tutorials/pretrain_tinyllama.md) and [finetuning](tutorials/finetune.md) to optimize models on excisting or custom datasets. Below is an example showing how to finetune a model with LoRA: @@ -324,6 +329,12 @@ If you have general questions about building with LitGPT, please [join our Disco ## Tutorials, how-to guides, and docs + +> [!NOTE] +> We recommend starting with the **[Zero to Pretraining, Finetuning, and Using LLMs with LitGPT](https://chat.openai.com/c/tutorial/0_to_litgpt.md)** if you are looking to get started with using LitGPT. + +Tutorials and in-depth feature documentation can be found below: + - Finetuning, incl. LoRA, QLoRA, and Adapters ([tutorials/finetune.md](tutorials/finetune.md)) - Pretraining ([tutorials/pretrain_tinyllama.md](tutorials/pretrain_tinyllama.md)) - Model evaluation ([tutorials/evaluation.md](tutorials/evaluation.md)) diff --git a/tutorials/0_to_litgpt.md b/tutorials/0_to_litgpt.md index 3c38948c16..bb3bbd5bdd 100644 --- a/tutorials/0_to_litgpt.md +++ b/tutorials/0_to_litgpt.md @@ -2,17 +2,17 @@ -This tutorial walks you through the main features and usage patterns for the LitGPT, a library for pretraining, finetuning, and using LLMs that focuses on an efficient user experience while being developer friendly. +This tutorial walks you through the main features and usage patterns for ⚡️LitGPT, a library for pretraining, finetuning, and using LLMs that focuses on an efficient user experience while being developer-friendly. -The topics, following the installation of LitGPT, are in chronological order reflecting the steps in an LLM lifecyle: Pretraining → Finetuning → Inference. +The topics, following the installation of LitGPT, are in chronological order, reflecting the steps in an LLM lifecycle: Pretraining → Finetuning → Inference.   - +   - +   @@ -43,9 +43,9 @@ pip install -e '.[all]'   ## Pretrain LLMs -Finetuning LLMs requires substantial compute resources and time commitment. For that reason, most researchers and practitioners prefer to skip step and prefer to skip this step, downloading a pretrained LLM instead. +Finetuning LLMs requires substantial compute resources and time commitment. For that reason, most researchers and practitioners prefer to skip this step and continue with the Download pretrained model weights section instead. -However, if you are feeling adventerous and want to pretrain your own LLM, here's how. +However, if you feel adventurous and want to pretrain your own LLM, here's how. First, we have to decide which type of model architecture we want to use. We list the available architectures by using the `pretrain` command without any additional arguments: @@ -70,7 +70,11 @@ vicuna-7b-v1.5 vicuna-7b-v1.5-16k ``` -Suppose we are interested in pretraining 1.1B parameter small `tiny-llama-1.1b` model. Before we can start finetuning, we also need to choose an download a tokenizer. We can do this via the `download` command. Note that running `litgpt download` without any additional arguments will also print a list of all available models and tokenizers to download. To filter for specific models, e.g., TinyLlama, we can use the `grep` command in our terminal: +Suppose we want to pretraining the 1.1B parameter small `tiny-llama-1.1b` model. Before starting finetuning, we must also choose and download a tokenizer. + +We can download a tokenizer via the `download` command. Note that running `litgpt download` without any additional arguments will also print a list of all available models and tokenizers to download. + +To filter for specific models, e.g., TinyLlama, we can use the `grep` command in our terminal: ```bash litgpt download | grep TinyLlama @@ -83,7 +87,7 @@ TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T TinyLlama/TinyLlama-1.1B-Chat-v1.0 ``` -Let's now download the tokenizer corresponding to `TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T` that we can then use to pretrain the TinyLlama model, which save the download tokenizer to a `checkpoints/` folder by default: +Let's now download the tokenizer corresponding to `TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T` that we can then use to pretrain the TinyLlama model, which saves the download tokenizer to a `checkpoints/` folder by default: ``` litgpt download \ @@ -93,11 +97,11 @@ litgpt download \   - +   -Next, we can pretrain the model on the OpenWebText dataset with default setting as follows: +Next, we can pretrain the model on the OpenWebText dataset with the default setting as follows: ```bash litgpt pretrain \ @@ -115,13 +119,13 @@ litgpt pretrain --help   > [!TIP] -> Above, we only covered the most basic commands for pretraining a model using LitGPT. If you are interested in pretraining a model, we highly recommend checking the resources below. +> Above, we only covered the most basic commands for pretraining a model using LitGPT. We highly recommend checking the resources below if you are interested in pretraining a model.   **More information and additional resources** -- [tutorials/pretrain_tinyllama](./pretrain_tinyllama.md): A tutorials for finetuning a 1.1B TinyLlama model on 3 trillion tokens +- [tutorials/pretrain_tinyllama](./pretrain_tinyllama.md): A tutorial for finetuning a 1.1B TinyLlama model on 3 trillion tokens - Project templates in reproducible environments with multi-GPU and multi-node support: - [Prepare the TinyLlama 1T token dataset](https://lightning.ai/lightning-ai/studios/prepare-the-tinyllama-1t-token-dataset) - [Pretrain LLMs - TinyLlama 1.1B](https://lightning.ai/lightning-ai/studios/pretrain-llms-tinyllama-1-1b) @@ -131,7 +135,7 @@ litgpt pretrain --help   ## Download pretrained model weights -Most practical usecases, like inference (/chat) or finetuning with LLMs, involve pretrained model weights. LitGPT supports a large number of model weights, which can be listed by executing the `download` command without any additional arguments: +Most practical use cases, like LLM inference (/chat) or finetuning, involve using pretrained model weights. LitGPT supports a large number of model weights, which can be listed by executing the `download` command without any additional arguments: ```bash litgpt download @@ -151,7 +155,7 @@ mistralai/Mixtral-8x7B-Instruct-v0.1 ... ``` -To download the model weights, simply provide one of the model strings above as a `--repo_id` argument: +To download the model weights, provide one of the model strings above as a `--repo_id` argument: ```bash litgpt download --repo_id microsoft/phi-2 @@ -172,7 +176,7 @@ Saving converted checkpoint to checkpoints/microsoft/phi-2   > [!TIP] -> Note that some models, such as Llama 2, require that you accept Meta AI's terms of service for this model, and you need to use a special access token via the `litgpt download ... --access_token ...` option. For more information, go to the respective Model Hub website, e.g., [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf). The access token can be created under your Model Hub in the Profile > Access Tokens menu. +> Note that some models, such as Llama 2, require that you accept Meta AI's terms of service for this model, and you need to use a special access token via the `litgpt download ... --access_token ...` option. For more information, visit the respective Model Hub website, e.g., [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf). The access token can be created under your Model Hub in the `Profile > Access Tokens` menu.   @@ -224,29 +228,29 @@ Time for inference: 1.26 sec total, 27.81 tokens/sec, 35 tokens   ## Finetune LLMs -LitGPT supports several methods supervised instruction finetuning, which allows you to finetune models to follow instructions. +LitGPT supports several methods of supervised instruction finetuning, which allows you to finetune models to follow instructions. Datasets for Instruction-finetuning are usually formatted in the following way:   - +   Alternatively, datasets for instruction finetuning can also contain an `'input'` field: -In an instruction-finetuning context, "full" finetuning means that all model parameters are updated. Adapter and LoRA (short for low-rank adaptation) are methods for parameter-efficient finetuning that only require updating a small fraction of the model weights. +In an instruction-finetuning context, "full" finetuning means updating all model parameters as opposed to only a subset. Adapter and LoRA (short for low-rank adaptation) are methods for parameter-efficient finetuning that only require updating a small fraction of the model weights.   - +   Parameter-efficient finetuning is much more resource-efficient and cheaper than full finetuning, and it often results in the same good performance on downstream tasks. -In the following example, we will use LoRA for finetuning, which is one of the most popular LLM finetuning method. (For more information on how LoRA works, please see [Code LoRA from Scratch](https://lightning.ai/lightning-ai/studios/code-lora-from-scratch).) +In the following example, we will use LoRA for finetuning, which is one of the most popular LLM finetuning methods. (For more information on how LoRA works, please see [Code LoRA from Scratch](https://lightning.ai/lightning-ai/studios/code-lora-from-scratch).) Before we start, we have to download a model as explained in the previous "Download pretrained model" section above: @@ -254,7 +258,7 @@ Before we start, we have to download a model as explained in the previous "Downl litgpt download --repo_id microsoft/phi-2 ``` -The LitGPT interface can be used with via command line arguments and configuration files. We recommend starting out with the configuration files from the [config_hub](../config_hub) and either modifying them directly or overrriding certain settings via the command line. For example, we can use the following setting to train the downloaded 2.7B parameter `microsoft/phi-2` model, where we set `--max_steps 5` for a quick test run. +The LitGPT interface can be used via command line arguments and configuration files. We recommend starting with the configuration files from the [config_hub](../config_hub) and either modifying them directly or overriding specific settings via the command line. For example, we can use the following setting to train the downloaded 2.7B parameter `microsoft/phi-2` model, where we set `--max_steps 5` for a quick test run. If you have downloaded or cloned the LitGPT repository, you can provide the `config` file via a relative path: @@ -264,7 +268,7 @@ litgpt finetune lora \ --train.max_steps 5 ``` -Alternatively, you can provide an URL: +Alternatively, you can provide a URL: ```bash litgpt finetune lora \ @@ -327,7 +331,7 @@ Running the previous finetuning command will initiate the finetuning process, wh min_lr=6e-05)} Seed set to 1337 Number of trainable parameters: 12,226,560 -Number of non trainable parameters: 2,779,683,840 +Number of non-trainable parameters: 2,779,683,840 The longest sequence length in the train data is 512, the model's maximum sequence length is 512 and context length is 2048 Validating ... Recommend a movie for me to watch during the weekend and explain the reason. @@ -370,7 +374,7 @@ To exit, press 'Enter' on an empty prompt. Seed set to 1234 >> Prompt: Why are LLMs so useful? ->> Reply: LLMs are useful because they can be trained to perform various natural language tasks, such as language translation, text generation, and question answering. They are also able to understand the context of the input data, which makes them particularly useful for tasks such as sentiment analysis and text summarization. Additionally, because LLMs can learn from large amounts of data, they are able to generalize well and perform well on new data. +>> Reply: LLMs are useful because they can be trained to perform various natural language tasks, such as language translation, text generation, and question-answering. They are also able to understand the context of the input data, which makes them particularly useful for tasks such as sentiment analysis and text summarization. Additionally, because LLMs can learn from large amounts of data, they are able to generalize well and perform well on new data. Time for inference: 2.15 sec total, 39.57 tokens/sec, 85 tokens @@ -416,10 +420,10 @@ Saving converted checkpoint to checkpoints/microsoft/phi-2 -Then, chat with the model using +Then, chat with the model using the following command: ```bash -litgpt chat --checkpoint_dir +litgpt chat --checkpoint_dir checkpoints/microsoft/phi-2 ``` ``` @@ -428,13 +432,13 @@ To exit, press 'Enter' on an empty prompt. Seed set to 1234 >> Prompt: What is the main difference between a large language model and a traditional search engine? ->> Reply: A large language model uses deep learning algorithms to analyze and generate natural language, while a traditional search engine uses algorithms to retrieve information from webpages. +>> Reply: A large language model uses deep learning algorithms to analyze and generate natural language, while a traditional search engine uses algorithms to retrieve information from web pages. Time for inference: 1.14 sec total, 26.26 tokens/sec, 30 tokens ``` > [!TIP] -> Most model weights are already represented in an efficient bfloat16 format. However, if the model currently exceeds your GPU memory, you can try to pass the `--precision bf16-true` option. In addition, you can check the quantization documentation for further optimization linked below. +> Most model weights are already represented in an efficient bfloat16 format. However, if the model currently exceeds your GPU memory, you can try to pass the `--precision bf16-true` option. In addition, you can check the quantization documentation for further optimization, which is linked below.   @@ -444,24 +448,102 @@ Time for inference: 1.14 sec total, 26.26 tokens/sec, 30 tokens - [tutorials/quantize](quantize.md): Quantizing models to reduce GPU memory requirements -## Evaluation -When you are deciding which model to use or evaluating finetuning results, it may be useful to find out how well a model is performing. For this purpose, you can use the integrated evaluation utility based on the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). + +  +## Converting LitGPT model weights to `safetensors` format + +Sometimes, it can be useful to convert LitGPT model weights for third-party and external tools. For example, we can convert a LitGPT model to the Hugging Face format and save it via `.safetensors` files. + +The `--checkpoint_dir` argument provided below points to a directory corresponding to a downloaded or finetuned model (see the *Download pretrained model* or *Finetune LLMs* sections above for more information): + + +```bash +litgpt convert from_litgpt \ + --checkpoint_dir checkpoints/microsoft/phi-2 \ + --output_dir out/converted_model/ +``` + +Certain tools like the `.from_pretrained` method in Hugging Face `transformers` also require the original `config.json` file that originally came with the downloaded model: + +```bash +cp checkpoints/microsoft/phi-2/config.json out/converted_model/config.json +``` + +You can now load the model into a Hugging Face transformers model and safe it in a `.safetensors` format as follows: + +```bash +import torch +from transformers import AutoModel + +# Load model +state_dict = torch.load('out/converted_model/model.pth') +model = AutoModel.from_pretrained( + "microsoft/phi-2", state_dict=state_dict +) + +# Save .safetensors files +model.save_pretrained("out/converted_model/") +``` + +``` +⚡ ~/litgpt ls -lh out/converted_model +total 16G +-rwxr--r-- 1 sebastian sebastian 891 Mar 20 17:08 config.json +-rw-r--r-- 1 sebastian sebastian 4.7G Mar 20 17:08 model-00001-of-00003.safetensors +-rw-r--r-- 1 sebastian sebastian 4.7G Mar 20 17:09 model-00002-of-00003.safetensors +-rw-r--r-- 1 sebastian sebastian 601M Mar 20 17:09 model-00003-of-00003.safetensors +-rw-r--r-- 1 sebastian sebastian 5.2G Mar 20 16:30 model.pth +-rw-r--r-- 1 sebastian sebastian 33K Mar 20 17:09 model.safetensors.index.json +``` + +You can then use the model with external tools, for example, Eleuther AI's [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) (see the `lm_eval` installation instructions [here](https://github.com/EleutherAI/lm-evaluation-harness?tab=readme-ov-file#install)). + +The LM Evaluation Harness requires a tokenizer to be present in the model checkpoint folder, which we can copy from the original download checkpoint: + +```bash +# Copy the tokenizer needed by the Eval Harness +cp checkpoints/microsoft/phi-2/tokenizer* +out/converted_model +``` + +Then, we can run the Evaluation Harness as follows: + +```bash +lm_eval --model hf \ + --model_args pretrained="out/converted_model" \ + --tasks "hellaswag,gsm8k,truthfulqa_mc2,mmlu,winogrande,arc_challenge" \ + --device "cuda:0" \ + --batch_size 4 +``` + +  > [!TIP] -> Use the `litgpt eval --help` command to see additional options. +> The Evaluation Harness tasks above are those used in Open LLM Leaderboard. You can find a list all supported tasks [here](https://github.com/EleutherAI/lm-evaluation-harness/blob/master/docs/task_table.md). + +   **More information and additional resources** -- [tutorials/evaluation](evaluation.md) -- [tutorials/convert_lit_models](convert_lit_models.md) +- [tutorials/convert_lit_models]: Tutorial on converting LitGPT weights + + + +  + +## Get involved! +We appreciate your feedback and contributions. If you have feature requests, questions, or want to contribute code or config files, please don't hesitate to use the [GitHub Issue](https://github.com/Lightning-AI/litgpt/issues) tracker. ---- +We welcome all individual contributors, regardless of their level of experience or hardware. Your contributions are valuable, and we are excited to see what you can accomplish in this collaborative and supportive environment. -TODOS: +  + +> [!TIP] +> Unsure about contributing? Check out our [How to Contribute to LitGPT](https://lightning.ai/pages/community/tutorial/how-to-contribute-to-litgpt/) guide. + +  -- [ ] Proofread -- [ ] Compress images to webp -- [ ] Link in Readme \ No newline at end of file +If you have general questions about building with LitGPT, please [join our Discord](https://discord.gg/VptPCZkGNa). From 62a82e4133cff8d3fb278bcbc58dedce229c3409 Mon Sep 17 00:00:00 2001 From: Sebastian Raschka Date: Tue, 26 Mar 2024 15:31:09 -0500 Subject: [PATCH 07/13] Mention config files --- tutorials/0_to_litgpt.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorials/0_to_litgpt.md b/tutorials/0_to_litgpt.md index bb3bbd5bdd..22b660875f 100644 --- a/tutorials/0_to_litgpt.md +++ b/tutorials/0_to_litgpt.md @@ -126,6 +126,7 @@ litgpt pretrain --help **More information and additional resources** - [tutorials/pretrain_tinyllama](./pretrain_tinyllama.md): A tutorial for finetuning a 1.1B TinyLlama model on 3 trillion tokens +- [config_hub/pretrain](../[config_hub/pretrain): Pre-made config files for pretraining that work well out of the box - Project templates in reproducible environments with multi-GPU and multi-node support: - [Prepare the TinyLlama 1T token dataset](https://lightning.ai/lightning-ai/studios/prepare-the-tinyllama-1t-token-dataset) - [Pretrain LLMs - TinyLlama 1.1B](https://lightning.ai/lightning-ai/studios/pretrain-llms-tinyllama-1-1b) @@ -393,8 +394,7 @@ Time for inference: 2.15 sec total, 39.57 tokens/sec, 85 tokens - [tutorials/finetune_lora](finetune_lora.md): Options for parameter-efficient finetuning with LoRA and QLoRA - [tutorials/finetune_adapter](finetune_adapter.md): A description of the parameter-efficient Llama-Adapter methods supported in LitGPT - [tutorials/oom](oom.md): Tips for dealing with out-of-memory (OOM) errors - -TODO: Mention config file resources +- [config_hub/finetune](../[config_hub/finetune): Pre-made config files for finetuning that work well out of the box   ## Inference (/chat) From c07f3eb9d20847fc5bafaed659229ce1aae81412 Mon Sep 17 00:00:00 2001 From: Sebastian Raschka Date: Tue, 26 Mar 2024 15:33:04 -0500 Subject: [PATCH 08/13] Update 0_to_litgpt.md --- tutorials/0_to_litgpt.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/0_to_litgpt.md b/tutorials/0_to_litgpt.md index 22b660875f..3949ae010b 100644 --- a/tutorials/0_to_litgpt.md +++ b/tutorials/0_to_litgpt.md @@ -397,7 +397,7 @@ Time for inference: 2.15 sec total, 39.57 tokens/sec, 85 tokens - [config_hub/finetune](../[config_hub/finetune): Pre-made config files for finetuning that work well out of the box   -## Inference (/chat) +## LLM inference To use a downloaded or finetuned model for chat, you only need to provide the corresponding checkpoint directory containing the model and tokenizer files. For example, to chat with the phi-2 model from Microsoft, download it as follows, as described in the "Download pretrained model" section: From 69f25945ab43b60fafdf41c63ebd8f1ac3734161 Mon Sep 17 00:00:00 2001 From: Sebastian Raschka Date: Tue, 26 Mar 2024 15:35:26 -0500 Subject: [PATCH 09/13] Fix URL --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7719236aeb..319d83f23f 100644 --- a/README.md +++ b/README.md @@ -35,7 +35,7 @@ ✅  [Quantization](tutorials/quantize.md): 4-bit floats, 8-bit integers, and double quantization. -✅  [Exporting](https://github.com/Lightning-AI/litgpt/blob/wip/tutorials/convert_lit_models.md) to other popular model weight formats. +✅  [Exporting](tutorials/convert_lit_models.md) to other popular model weight formats. ✅  Many popular datasets for [pretraining](tutorials/pretrain_tinyllama.md) and [finetuning](tutorials/prepare_dataset.md), and [support for custom datasets](tutorials/prepare_dataset.md#preparing-custom-datasets-for-instruction-finetuning). From 02991074227b62b8e8e327867c587a498699830c Mon Sep 17 00:00:00 2001 From: Sebastian Raschka Date: Tue, 26 Mar 2024 15:36:00 -0500 Subject: [PATCH 10/13] Fix another link --- tutorials/0_to_litgpt.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/0_to_litgpt.md b/tutorials/0_to_litgpt.md index 3949ae010b..08d6c214dc 100644 --- a/tutorials/0_to_litgpt.md +++ b/tutorials/0_to_litgpt.md @@ -527,7 +527,7 @@ lm_eval --model hf \   **More information and additional resources** -- [tutorials/convert_lit_models]: Tutorial on converting LitGPT weights +- [tutorials/convert_lit_models](tutorials/convert_lit_models.md): Tutorial on converting LitGPT weights From 6d9a9d8759df38f2dc4888e50a4cb107ae4067d4 Mon Sep 17 00:00:00 2001 From: Sebastian Raschka Date: Tue, 26 Mar 2024 15:38:58 -0500 Subject: [PATCH 11/13] Update tutorials/0_to_litgpt.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Carlos Mocholí --- tutorials/0_to_litgpt.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/0_to_litgpt.md b/tutorials/0_to_litgpt.md index 08d6c214dc..c4a910f00c 100644 --- a/tutorials/0_to_litgpt.md +++ b/tutorials/0_to_litgpt.md @@ -394,7 +394,7 @@ Time for inference: 2.15 sec total, 39.57 tokens/sec, 85 tokens - [tutorials/finetune_lora](finetune_lora.md): Options for parameter-efficient finetuning with LoRA and QLoRA - [tutorials/finetune_adapter](finetune_adapter.md): A description of the parameter-efficient Llama-Adapter methods supported in LitGPT - [tutorials/oom](oom.md): Tips for dealing with out-of-memory (OOM) errors -- [config_hub/finetune](../[config_hub/finetune): Pre-made config files for finetuning that work well out of the box +- [config_hub/finetune](../config_hub/finetune): Pre-made config files for finetuning that work well out of the box   ## LLM inference From 059b471aca84711fcc56bf3269ccd775dfcddf7a Mon Sep 17 00:00:00 2001 From: Sebastian Raschka Date: Tue, 26 Mar 2024 15:39:06 -0500 Subject: [PATCH 12/13] Update tutorials/0_to_litgpt.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Carlos Mocholí --- tutorials/0_to_litgpt.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/0_to_litgpt.md b/tutorials/0_to_litgpt.md index c4a910f00c..6b5dfe6f5e 100644 --- a/tutorials/0_to_litgpt.md +++ b/tutorials/0_to_litgpt.md @@ -126,7 +126,7 @@ litgpt pretrain --help **More information and additional resources** - [tutorials/pretrain_tinyllama](./pretrain_tinyllama.md): A tutorial for finetuning a 1.1B TinyLlama model on 3 trillion tokens -- [config_hub/pretrain](../[config_hub/pretrain): Pre-made config files for pretraining that work well out of the box +- [config_hub/pretrain](../config_hub/pretrain): Pre-made config files for pretraining that work well out of the box - Project templates in reproducible environments with multi-GPU and multi-node support: - [Prepare the TinyLlama 1T token dataset](https://lightning.ai/lightning-ai/studios/prepare-the-tinyllama-1t-token-dataset) - [Pretrain LLMs - TinyLlama 1.1B](https://lightning.ai/lightning-ai/studios/pretrain-llms-tinyllama-1-1b) From 0839f1f11ebe09a68962104c492fd2302ce121f2 Mon Sep 17 00:00:00 2001 From: Sebastian Raschka Date: Thu, 28 Mar 2024 11:50:19 -0500 Subject: [PATCH 13/13] Update 0_to_litgpt.md --- tutorials/0_to_litgpt.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tutorials/0_to_litgpt.md b/tutorials/0_to_litgpt.md index 6b5dfe6f5e..415190fec6 100644 --- a/tutorials/0_to_litgpt.md +++ b/tutorials/0_to_litgpt.md @@ -1,4 +1,4 @@ -# Zero to Pretraining, Finetuning, and Using LLMs with LitGPT +# Zero to LitGPT: Getting Started with Pretraining, Finetuning, and Using LLMs