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--- |
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language: |
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- en |
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tags: |
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- pytorch |
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- causal-lm |
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- pythia |
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license: apache-2.0 |
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datasets: |
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- EleutherAI/the_pile_deduplicated |
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--- |
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Converted and quantized from EleutherAI's Pythia Deduped checkpoints. |
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Notes: |
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- Converted with ggerganov/ggml's gpt-neox conversion script, and tested with KoboldCpp. |
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- I can't promise that this will work, especially with other frontends. I've had problems when generating words like "Alice" or "Hakurei" / "Gensokyo". Could be related to the ggml implementation of GPT-NeoX having a "hacked" tokenizer [(source)](https://github.com/ggerganov/ggml/tree/master/examples/gpt-neox#notes). |
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Versions: |
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**2023-04-20:** *q4_3. Used [commit 05f3079](https://github.com/ggerganov/ggml/tree/05f307971862b83df12fada0c42ee027ba5a82b5/examples/stablelm).* |
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**2023-04-30:** *q5_0, q5_1, and q8_0, up to 2.8B. I can't upload all conversions of 6.9B and 12B due to my internet. Used [commit 5dd92f4](https://github.com/ggerganov/ggml/tree/5dd92f421ee44f18b8fde0afbf5ca8fc7bf93841/examples/stablelm).* |
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**2023-05-06:** *q4_0 and q4_2, up to 2.8B. Used [commit ff6e03c](https://github.com/ggerganov/ggml/tree/ff6e03cbcd9bf6e9fa41d49f2495c042efae4dc6/examples/stablelm).* |
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**2023-05-15:** **RECOMMENDED** - *New quantization format. q4_0 and q5_1, up to 2.8B. Used [commit 010203f](https://github.com/ggerganov/ggml/tree/010203f94a85df5c86b773dc5acb698c8e7b1e7b/examples/gpt-neox).* |
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They're separated by date and commit so it's easier to track any breaking changes. |
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# RAM USAGE (on KoboldCpp w/ OpenBLAS) |
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Model | Initial RAM | After generation |
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:--:|:--:|:--: |
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Unloaded | 41.3 MiB |
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ggml-pythia-70m-deduped-q4_0.bin | 113.3 MiB | 267.8 MiB |
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ggml-pythia-70m-deduped-q5_1.bin | 121.5 MiB | 129.4 MiB |
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ggml-pythia-160m-deduped-q4_0.bin | 199.4 MiB | 201.6 MiB |
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ggml-pythia-160m-deduped-q5_1.bin | 227.5 MiB | 241.0 MiB |
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ggml-pythia-410m-deduped-q4_0.bin | 399.2 MiB | 406.2 MiB |
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ggml-pythia-410m-deduped-q5_1.bin | 455.7 MiB | 460.3 MiB |
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ggml-pythia-1b-deduped-q4_0.bin | 803.0 MiB | 809.0 MiB |
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ggml-pythia-1b-deduped-q5_1.bin | 921.5 MiB | 927.3 MiB |
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ggml-pythia-1.4b-deduped-q4_0.bin | 1.1 GiB | 1.1 GiB |
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ggml-pythia-1.4b-deduped-q5_1.bin | 1.3 GiB | 1.3 GiB |
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ggml-pythia-2.8b-deduped-q4_0.bin | 2.0 GiB | 2.0 GiB |
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ggml-pythia-2.8b-deduped-q5_1.bin | 2.4 GiB | 2.4 GiB |
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# ALTERNATIVES |
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If you're here because you want a smaller model to run on a device with constrained memory, consider the following: |
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- OpenLLaMA [3B](https://huggingface.co/openlm-research/open_llama_3b_350bt_preview) [(7B)](https://huggingface.co/openlm-research/open_llama_7b_400bt_preview) |
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- RedPajama-INCITE [(3B)](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1) [(7B)](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) |
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- MPT [(1B)](https://huggingface.co/mosaicml/mpt-1b-redpajama-200b) [(7B)](https://huggingface.co/mosaicml/mpt-7b). |
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- RWKV PilePlus [(169M) (430M) (1.5B) (3B)](https://huggingface.co/BlinkDL/rwkv-4-pileplus) |
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All of them are trained at least partially on an open reproduction of LLaMA's dataset, [RedPajama](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T), but they're based on different architectures. OpenLLaMA is based on the LLaMA architecture (making it compatible with llama.cpp), RedPajama-INCITE is based on GPT-NeoX, and MPT and RWKV use their own. |
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Below is the original model card for Pythia 1.4B Deduped. |
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* * * |
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The *Pythia Scaling Suite* is a collection of models developed to facilitate |
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interpretability research. It contains two sets of eight models of sizes |
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70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two |
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models: one trained on the Pile, and one trained on the Pile after the dataset |
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has been globally deduplicated. All 8 model sizes are trained on the exact |
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same data, in the exact same order. We also provide 154 intermediate |
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checkpoints per model, hosted on Hugging Face as branches. |
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The Pythia model suite was designed to promote scientific |
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research on large language models, especially interpretability research. |
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Despite not centering downstream performance as a design goal, we find the |
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models <a href="#evaluations">match or exceed</a> the performance of |
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similar and same-sized models, such as those in the OPT and GPT-Neo suites. |
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<details> |
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<summary style="font-weight:600">Details on previous early release and naming convention.</summary> |
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Previously, we released an early version of the Pythia suite to the public. |
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However, we decided to retrain the model suite to address a few hyperparameter |
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discrepancies. This model card <a href="#changelog">lists the changes</a>; |
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see appendix B in the Pythia paper for further discussion. We found no |
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difference in benchmark performance between the two Pythia versions. |
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The old models are |
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[still available](https://huggingface.co/models?other=pythia_v0), but we |
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suggest the retrained suite if you are just starting to use Pythia.<br> |
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**This is the current release.** |
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Please note that all models in the *Pythia* suite were renamed in January |
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2023. For clarity, a <a href="#naming-convention-and-parameter-count">table |
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comparing the old and new names</a> is provided in this model card, together |
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with exact parameter counts. |
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</details> |
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<br> |
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# Pythia-1.4B-deduped |
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## Model Details |
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- Developed by: [EleutherAI](http://eleuther.ai) |
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- Model type: Transformer-based Language Model |
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- Language: English |
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- Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) |
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for training procedure, config files, and details on how to use. |
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- Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) |
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- License: Apache 2.0 |
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- Contact: to ask questions about this model, join the [EleutherAI |
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Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. |
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Please read the existing *Pythia* documentation before asking about it in the |
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EleutherAI Discord. For general correspondence: [contact@eleuther. |
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ai](mailto:contact@eleuther.ai). |
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<figure> |
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| Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models | |
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| -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: | |
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| 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10<sup>-3</sup> | — | |
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| 160M | 85,056,000 | 12 | 768 | 12 | 4M | 6.0 x 10<sup>-4</sup> | GPT-Neo 125M, OPT-125M | |
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| 410M | 302,311,424 | 24 | 1024 | 16 | 4M | 3.0 x 10<sup>-4</sup> | OPT-350M | |
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| 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10<sup>-4</sup> | — | |
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| 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 4M | 2.0 x 10<sup>-4</sup> | GPT-Neo 1.3B, OPT-1.3B | |
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| 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10<sup>-4</sup> | GPT-Neo 2.7B, OPT-2.7B | |
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| 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10<sup>-4</sup> | OPT-6.7B | |
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| 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10<sup>-4</sup> | — | |
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<figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and |
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non-deduped models of a given size have the same hyperparameters. “Equivalent” |
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models have <b>exactly</b> the same architecture, and the same number of |
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non-embedding parameters.</figcaption> |
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</figure> |
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## Uses and Limitations |
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### Intended Use |
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The primary intended use of Pythia is research on the behavior, functionality, |
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and limitations of large language models. This suite is intended to provide |
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a controlled setting for performing scientific experiments. We also provide |
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154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints |
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`step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to |
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`step143000`. These checkpoints are hosted on Hugging Face as branches. Note |
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that branch `143000` corresponds exactly to the model checkpoint on the `main` |
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branch of each model. |
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You may also further fine-tune and adapt Pythia-1.4B-deduped for deployment, |
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as long as your use is in accordance with the Apache 2.0 license. Pythia |
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models work with the Hugging Face [Transformers |
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Library](https://huggingface.co/docs/transformers/index). If you decide to use |
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pre-trained Pythia-1.4B-deduped as a basis for your fine-tuned model, please |
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conduct your own risk and bias assessment. |
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### Out-of-scope use |
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The Pythia Suite is **not** intended for deployment. It is not a in itself |
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a product and cannot be used for human-facing interactions. For example, |
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the model may generate harmful or offensive text. Please evaluate the risks |
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associated with your particular use case. |
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Pythia models are English-language only, and are not suitable for translation |
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or generating text in other languages. |
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Pythia-1.4B-deduped has not been fine-tuned for downstream contexts in which |
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language models are commonly deployed, such as writing genre prose, |
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or commercial chatbots. This means Pythia-1.4B-deduped will **not** |
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respond to a given prompt the way a product like ChatGPT does. This is because, |
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unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement |
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Learning from Human Feedback (RLHF) to better “follow” human instructions. |
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### Limitations and biases |
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The core functionality of a large language model is to take a string of text |
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and predict the next token. The token used by the model need not produce the |
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most “accurate” text. Never rely on Pythia-1.4B-deduped to produce factually accurate |
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output. |
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This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset |
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known to contain profanity and texts that are lewd or otherwise offensive. |
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See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a |
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discussion of documented biases with regards to gender, religion, and race. |
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Pythia-1.4B-deduped may produce socially unacceptable or undesirable text, *even if* |
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the prompt itself does not include anything explicitly offensive. |
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If you plan on using text generated through, for example, the Hosted Inference |
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API, we recommend having a human curate the outputs of this language model |
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before presenting it to other people. Please inform your audience that the |
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text was generated by Pythia-1.4B-deduped. |
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### Quickstart |
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Pythia models can be loaded and used via the following code, demonstrated here |
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for the third `pythia-70m-deduped` checkpoint: |
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```python |
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from transformers import GPTNeoXForCausalLM, AutoTokenizer |
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model = GPTNeoXForCausalLM.from_pretrained( |
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"EleutherAI/pythia-70m-deduped", |
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revision="step3000", |
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cache_dir="./pythia-70m-deduped/step3000", |
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) |
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tokenizer = AutoTokenizer.from_pretrained( |
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"EleutherAI/pythia-70m-deduped", |
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revision="step3000", |
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cache_dir="./pythia-70m-deduped/step3000", |
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) |
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inputs = tokenizer("Hello, I am", return_tensors="pt") |
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tokens = model.generate(**inputs) |
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tokenizer.decode(tokens[0]) |
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``` |
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Revision/branch `step143000` corresponds exactly to the model checkpoint on |
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the `main` branch of each model.<br> |
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For more information on how to use all Pythia models, see [documentation on |
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GitHub](https://github.com/EleutherAI/pythia). |
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## Training |
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### Training data |
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Pythia-1.4B-deduped was trained on the Pile **after the dataset has been globally |
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deduplicated**.<br> |
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[The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in |
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English. It was created by EleutherAI specifically for training large language |
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models. It contains texts from 22 diverse sources, roughly broken down into |
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five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), |
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prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and |
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miscellaneous (e.g. GitHub, Enron Emails). See [the Pile |
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paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, |
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methodology, and a discussion of ethical implications. Consult [the |
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datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation |
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about the Pile and its component datasets. The Pile can be downloaded from |
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the [official website](https://pile.eleuther.ai/), or from a [community |
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mirror](https://the-eye.eu/public/AI/pile/). |
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### Training procedure |
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All models were trained on the exact same data, in the exact same order. Each |
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model saw 299,892,736,000 tokens during training, and 143 checkpoints for each |
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model are saved every 2,097,152,000 tokens, spaced evenly throughout training, |
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from `step1000` to `step143000` (which is the same as `main`). In addition, we |
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also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`. |
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This corresponds to training for just under 1 epoch on the Pile for |
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non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. |
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All *Pythia* models trained for 143000 steps at a batch size |
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of 2M (2,097,152 tokens).<br> |
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See [GitHub](https://github.com/EleutherAI/pythia) for more details on training |
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procedure, including [how to reproduce |
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it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br> |
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Pythia uses the same tokenizer as [GPT-NeoX- |
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20B](https://huggingface.co/EleutherAI/gpt-neox-20b). |
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## Evaluations |
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All 16 *Pythia* models were evaluated using the [LM Evaluation |
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Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access |
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the results by model and step at `results/json/*` in the [GitHub |
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repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br> |
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Expand the sections below to see plots of evaluation results for all |
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Pythia and Pythia-deduped models compared with OPT and BLOOM. |
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<details> |
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<summary>LAMBADA – OpenAI</summary> |
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<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/> |
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</details> |
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<details> |
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<summary>Physical Interaction: Question Answering (PIQA)</summary> |
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<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/> |
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</details> |
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<details> |
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<summary>WinoGrande</summary> |
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<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/> |
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</details> |
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<details> |
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<summary>AI2 Reasoning Challenge—Easy Set</summary> |
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<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/> |
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</details> |
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<details> |
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<summary>SciQ</summary> |
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<img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/> |
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</details> |
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## Changelog |
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This section compares differences between previously released |
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[Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current |
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models. See Appendix B of the Pythia paper for further discussion of these |
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changes and the motivation behind them. We found that retraining Pythia had no |
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impact on benchmark performance. |
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- All model sizes are now trained with uniform batch size of 2M tokens. |
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Previously, the models of size 160M, 410M, and 1.4B parameters were trained |
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with batch sizes of 4M tokens. |
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- We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64, |
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128,256,512} in addition to every 1000 training steps. |
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- Flash Attention was used in the new retrained suite. |
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- We remedied a minor inconsistency that existed in the original suite: all |
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models of size 2.8B parameters or smaller had a learning rate (LR) schedule |
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which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and |
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12B models all used an LR schedule which decayed to a minimum LR of 0. In |
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the redone training runs, we rectified this inconsistency: all models now were |
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trained with LR decaying to a minimum of 0.1× their maximum LR. |
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|
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### Naming convention and parameter count |
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|
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*Pythia* models were renamed in January 2023. It is possible that the old |
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naming convention still persists in some documentation by accident. The |
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current naming convention (70M, 160M, etc.) is based on total parameter count. |
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<figure style="width:32em"> |
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| current Pythia suffix | old suffix | total params | non-embedding params | |
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| --------------------: | ---------: | -------------: | -------------------: | |
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| 70M | 19M | 70,426,624 | 18,915,328 | |
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| 160M | 125M | 162,322,944 | 85,056,000 | |
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| 410M | 350M | 405,334,016 | 302,311,424 | |
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| 1B | 800M | 1,011,781,632 | 805,736,448 | |
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| 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 | |
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| 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 | |
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| 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 | |
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| 12B | 13B | 11,846,072,320 | 11,327,027,200 | |
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</figure> |
