--- base_model: LumiOpen/Poro-34B datasets: - cerebras/SlimPajama-627B - bigcode/starcoderdata - mc4 - allenai/dolma inference: false license: apache-2.0 model_creator: LumiOpen model_name: Poro 34B model_type: bloom prompt_template: '{prompt} ' quantized_by: TheBloke ---
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# Poro 34B - GPTQ - Model creator: [LumiOpen](https://huggingface.co/LumiOpen) - Original model: [Poro 34B](https://huggingface.co/LumiOpen/Poro-34B) # Description This repo contains GPTQ model files for [LumiOpen's Poro 34B](https://huggingface.co/LumiOpen/Poro-34B). Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them. These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Poro-34B-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Poro-34B-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Poro-34B-GGUF) * [LumiOpen's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/LumiOpen/Poro-34B) ## Prompt template: None ``` {prompt} ``` ## Known compatible clients / servers GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models. These GPTQ models are known to work in the following inference servers/webuis. - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) - [KoboldAI United](https://github.com/henk717/koboldai) - [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui) - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) This may not be a complete list; if you know of others, please let me know! ## Provided files, and GPTQ parameters Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements. Each separate quant is in a different branch. See below for instructions on fetching from different branches. Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
Explanation of GPTQ parameters - Bits: The bit size of the quantised model. - GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value. - Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now. - Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy. - GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s). - Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences. - ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc | | ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- | | [main](https://huggingface.co/TheBloke/Poro-34B-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 20.35 GB | No | 4-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Poro-34B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 20.99 GB | No | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. | | [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Poro-34B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 22.94 GB | No | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. | | [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Poro-34B-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 16.79 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. | | [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Poro-34B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 37.00 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. | | [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/Poro-34B-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 18.65 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. | | [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Poro-34B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 37.77 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. | ## How to download, including from branches ### In text-generation-webui To download from the `main` branch, enter `TheBloke/Poro-34B-GPTQ` in the "Download model" box. To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Poro-34B-GPTQ:gptq-4bit-128g-actorder_True` ### From the command line I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` To download the `main` branch to a folder called `Poro-34B-GPTQ`: ```shell mkdir Poro-34B-GPTQ huggingface-cli download TheBloke/Poro-34B-GPTQ --local-dir Poro-34B-GPTQ --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: ```shell mkdir Poro-34B-GPTQ huggingface-cli download TheBloke/Poro-34B-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir Poro-34B-GPTQ --local-dir-use-symlinks False ```
More advanced huggingface-cli download usage If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model. The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`. For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell mkdir Poro-34B-GPTQ HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Poro-34B-GPTQ --local-dir Poro-34B-GPTQ --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
### With `git` (**not** recommended) To clone a specific branch with `git`, use a command like this: ```shell git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/Poro-34B-GPTQ ``` Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.) ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the **Model tab**. 2. Under **Download custom model or LoRA**, enter `TheBloke/Poro-34B-GPTQ`. - To download from a specific branch, enter for example `TheBloke/Poro-34B-GPTQ:gptq-4bit-128g-actorder_True` - see Provided Files above for the list of branches for each option. 3. Click **Download**. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to **Model**. 6. In the **Model** dropdown, choose the model you just downloaded: `Poro-34B-GPTQ` 7. The model will automatically load, and is now ready for use! 8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right. - Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`. 9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started! ## Serving this model from Text Generation Inference (TGI) It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/Poro-34B-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: {response}") ``` ## Python code example: inference from this GPTQ model ### Install the necessary packages Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later. ```shell pip3 install --upgrade transformers optimum # If using PyTorch 2.1 + CUDA 12.x: pip3 install --upgrade auto-gptq # or, if using PyTorch 2.1 + CUDA 11.x: pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ ``` If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source: ```shell pip3 uninstall -y auto-gptq git clone https://github.com/PanQiWei/AutoGPTQ cd AutoGPTQ git checkout v0.5.1 pip3 install . ``` ### Example Python code ```python from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_name_or_path = "TheBloke/Poro-34B-GPTQ" # To use a different branch, change revision # For example: revision="gptq-4bit-128g-actorder_True" model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto", trust_remote_code=False, revision="main") tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True) prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' print("\n\n*** Generate:") input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda() output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512) print(tokenizer.decode(output[0])) # Inference can also be done using transformers' pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` ## Compatibility The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly. [ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility. For a list of clients/servers, please see "Known compatible clients / servers", above. ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. # Original model card: LumiOpen's Poro 34B
# Poro 34B Model Card _**NOTE:** This is a **research checkpoint** of a model for which **training has not been completed.** It is being provided in its current state for research and testing purposes. **Care should be taken when using the outputs of the model.** Once pretraining has completed we intend to release additional instruction-tuned and chat-tuned varieties._ Poro is a 34B parameter decoder-only transformer pretrained on Finnish, English and code. It is being trained on 1 trillion tokens (500 billion as of this release). Poro is a fully open source model and is made available under the Apache 2.0 License. Poro was created in a collaboration between [SiloGen](https://www.silo.ai/silogen) from [Silo AI](https://www.silo.ai/), the [TurkuNLP group](https://turkunlp.org/) of the University of Turku, and [High Performance Language Technologies](https://hplt-project.org/) (HPLT). Training was conducted on the [LUMI supercomputer](https://www.lumi-supercomputer.eu/), using compute resources generously provided by [CSC](https://csc.fi/) - IT Center for Science, Finland. This project is part of an ongoing effort to create open source large language models for non-English and especially low resource languages like Finnish. Through the combination of English and Finnish training data we get a model that outperforms previous Finnish only models, while also being fluent in English and code, and capable of basic translation between English and Finnish. Poro 34B is only the first model of our model family. Work is already underway on our next models which will support additional languages, and include features like flash attention, rotary embeddings, and grouped query attention. _What does Poro mean?_ Poro is the Finnish word for Reindeer! 🦌 These animals are native to Finland and hold a significant and historical role in Finnish culture. ## Model Overview _**NOTE:** In addition to being an early research release, Poro is a base model which needs further fine tuning for most use cases._ Poro is a generative pretrained transformer using a BLOOM architecture, and makes use of ALiBi embeddings to support context length extrapolation at inference time. | Hyperparameter | Value | | :------------- | :----: | | n_parameters | 34.2B | | n_layers | 54 | | n_heads | 56 | | d_model | 7168 | | vocab_size | 128000 | | sequence_length | 2048 | ## Poro Research Checkpoints Checkpoints are available as branches in the repository. Checkpoints will be released roughly every 100B tokens. The main branch will always point to the latest checkpoint. The following checkpoints are available: * [100B](https://huggingface.co/LumiOpen/Poro-34B/tree/100B) * [200B](https://huggingface.co/LumiOpen/Poro-34B/tree/200B) * [300B](https://huggingface.co/LumiOpen/Poro-34B/tree/300B) * [400B](https://huggingface.co/LumiOpen/Poro-34B/tree/400B) * [500B](https://huggingface.co/LumiOpen/Poro-34B/tree/500B) The transformers library allows you to load a checkpoint from a branch as follows: ```python branch = "200B" model = transformers.AutoModelForCausalLM.from_pretrained( "LumiOpen/Poro-34B", torch_dtype=torch.bfloat16, revision=branch, ) ``` ## Training Poro was trained on the LUMI supercomputer, using 512 AMD MI250X GPUs. Each MI250X GPU has two Graphics Complex Dies (GCDs) for a world size of 1024 during training, using activation checkpointing, a micro batch size of 1, gradient accumulation of 16, and a 3D parallelism strategy of TP=2, PP=4, DP=128. Training began in September 2023 using a [custom fork](https://github.com/TurkuNLP/Megatron-DeepSpeed) of the Megatron-Deepspeed framework. ## Training Hyperparameters | Hyperparameter | Value | Comment | | :------------: | :---: | :------:| | Precision | bfloat16 | | | Optimizer | AdamW | | | Learning rate | 1.5e-4 | 10B tokens warm-up, cosine decay to 2e-5 | | Weight decay | 1e-1 | | | Batch size | 2048 | 2048 samples x 2048 tokens = 4194304 tokens | ## Tokenizer Poro uses a custom 128K Bloom tokenizer trained on the same English, Finnish and Code dataset used to train the model. ## Dataset Poro is being trained on a 1 trillion token mixed dataset of English, Finnish and Code. | Dataset | Notes | Percentage | Epochs | Tokens | | :-----: | :---: | :--------: | :----: | :----: | | SlimPajama | Excluding books3 data | 54.16% | 1x | 541.7B | | Finnish | TurkuNLP Finnish dataset | 13.05% | 4x | 131.5B | | Tatoeba | English/Finnish sentence pairs | 0.81% | 1x | 8.0B | | Starcoder | | 31.53% | 1.52x | 315.4B | | Project Gutenberg | from Dolma dataset | 0.46% | 1x | 4.5B | The Finnish dataset is a combination of many Finnish resources: * [Finnish Internet Parsebank](https://turkunlp.org/finnish_nlp.html) * [mC4 multilingual colossal, cleaned Common Crawl](https://huggingface.co/datasets/mc4) * [Common Crawl Finnish](https://github.com/turkunlp/CC-Fi) * [Finnish Wikipedia](https://fi.wikipedia.org/wiki) * [Lönnrot Projekti Lönnrot](http://www.lonnrot.net/) * [Suomi24 The Suomi 24 Corpus 2001-2020](http://urn.fi/urn:nbn:fi:lb-2021101527) * [Reddit r/Suomi submissions and comments](https://www.reddit.com/r/Suomi) * [STT Finnish News Agency Archive 1992-2018](http://urn.fi/urn:nbn:fi:lb-2019041501) * [Yle Finnish News Archive 2011-2018](http://urn.fi/urn:nbn:fi:lb-2017070501) * [Yle Finnish News Archive 2019-2020](http://urn.fi/urn:nbn:fi:lb-2021050401) * [Yle News Archive Easy-to-read Finnish 2011-2018](http://urn.fi/urn:nbn:fi:lb-2019050901) * [Yle News Archive Easy-to-read Finnish 2019-2020](http://urn.fi/urn:nbn:fi:lb-2021050701) ## Evaluation Results Despite the early training stage, Poro already exceeds the performance of the Finnish-only [FinGPT](https://turkunlp.org/gpt3-finnish) language models on the [FIN-bench](https://github.com/TurkuNLP/FIN-bench) Finnish language benchmark. Full evaluation results will be published with the final model. ## Ethical Considerations and Limitations _Poro 34B is a release of a partially trained model, and special care should be taken when using any output._ Poro is an advanced language model, primarily optimized for English, Finnish and code, with no meaningful proficiency in any other languages. As with most AI-driven systems, Poro is a product of the vast data it has been trained on, which may reflect the imperfections, biases, and idiosyncrasies of the wider web. Poro may, at times, produce outputs that can be considered inaccurate, prejudiced, or controversial. Users and developers engaging with Poro should exercise discretion and consider additional evaluation and customization to ensure the model's responses align with their specific needs and ethical standards. ## License Poro is released under the Apache 2.0 license.