--- language: - en license: llama2 datasets: - garage-bAInd/Open-Platypus - Open-Orca/OpenOrca model_name: Platypus2 70B Instruct inference: false model_creator: garage-bAInd model_link: https://huggingface.co/garage-bAInd/Platypus2-70B-instruct model_type: llama quantized_by: TheBloke base_model: garage-bAInd/Platypus2-70B-instruct ---
TheBlokeAI

Chat & support: TheBloke's Discord server

Want to contribute? TheBloke's Patreon page

TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)

# Platypus2 70B Instruct - GGML - Model creator: [garage-bAInd](https://huggingface.co/garage-bAInd) - Original model: [Platypus2 70B Instruct](https://huggingface.co/garage-bAInd/Platypus2-70B-instruct) ## Description This repo contains GGML format model files for [garage-bAInd's Platypus2 70B Instruct](https://huggingface.co/garage-bAInd/Platypus2-70B-instruct). ### Important note regarding GGML files. The GGML format has now been superseded by GGUF. As of August 21st 2023, [llama.cpp](https://github.com/ggerganov/llama.cpp) no longer supports GGML models. Third party clients and libraries are expected to still support it for a time, but many may also drop support. Please use the GGUF models instead. ### About GGML GPU acceleration is now available for Llama 2 70B GGML files, with both CUDA (NVidia) and Metal (macOS). The following clients/libraries are known to work with these files, including with GPU acceleration: * [llama.cpp](https://github.com/ggerganov/llama.cpp), commit `e76d630` and later. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), version 1.37 and later. A powerful GGML web UI, especially good for story telling. * [LM Studio](https://lmstudio.ai/), a fully featured local GUI with GPU acceleration for both Windows and macOS. Use 0.1.11 or later for macOS GPU acceleration with 70B models. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), version 0.1.77 and later. A Python library with LangChain support, and OpenAI-compatible API server. * [ctransformers](https://github.com/marella/ctransformers), version 0.2.15 and later. A Python library with LangChain support, and OpenAI-compatible API server. ## Repositories available * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GGUF) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GGML) * [garage-bAInd's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/garage-bAInd/Platypus2-70B-instruct) ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` ## Compatibility ### Works with llama.cpp [commit `e76d630`](https://github.com/ggerganov/llama.cpp/commit/e76d630df17e235e6b9ef416c45996765d2e36fb) until August 21st, 2023 Will not work with `llama.cpp` after commit [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa). For compatibility with latest llama.cpp, please use GGUF files instead. Or one of the other tools and libraries listed above. To use in llama.cpp, you must add `-gqa 8` argument. For other UIs and libraries, please check the docs. ## Explanation of the new k-quant methods
Click to see details The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type. Refer to the Provided Files table below to see what files use which methods, and how.
## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [platypus2-70b-instruct.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GGML/blob/main/platypus2-70b-instruct.ggmlv3.q2_K.bin) | q2_K | 2 | 28.59 GB| 31.09 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. | | [platypus2-70b-instruct.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GGML/blob/main/platypus2-70b-instruct.ggmlv3.q3_K_S.bin) | q3_K_S | 3 | 29.75 GB| 32.25 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors | | [platypus2-70b-instruct.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GGML/blob/main/platypus2-70b-instruct.ggmlv3.q3_K_M.bin) | q3_K_M | 3 | 33.04 GB| 35.54 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | [platypus2-70b-instruct.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GGML/blob/main/platypus2-70b-instruct.ggmlv3.q3_K_L.bin) | q3_K_L | 3 | 36.15 GB| 38.65 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K | | [platypus2-70b-instruct.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GGML/blob/main/platypus2-70b-instruct.ggmlv3.q4_0.bin) | q4_0 | 4 | 38.87 GB| 41.37 GB | Original quant method, 4-bit. | | [platypus2-70b-instruct.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GGML/blob/main/platypus2-70b-instruct.ggmlv3.q4_K_S.bin) | q4_K_S | 4 | 38.87 GB| 41.37 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors | | [platypus2-70b-instruct.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GGML/blob/main/platypus2-70b-instruct.ggmlv3.q4_K_M.bin) | q4_K_M | 4 | 41.38 GB| 43.88 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K | | [platypus2-70b-instruct.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GGML/blob/main/platypus2-70b-instruct.ggmlv3.q4_1.bin) | q4_1 | 4 | 43.17 GB| 45.67 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. | | [platypus2-70b-instruct.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GGML/blob/main/platypus2-70b-instruct.ggmlv3.q5_0.bin) | q5_0 | 5 | 47.46 GB| 49.96 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. | | [platypus2-70b-instruct.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GGML/blob/main/platypus2-70b-instruct.ggmlv3.q5_K_S.bin) | q5_K_S | 5 | 47.46 GB| 49.96 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors | | [platypus2-70b-instruct.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/Platypus2-70B-Instruct-GGML/blob/main/platypus2-70b-instruct.ggmlv3.q5_K_M.bin) | q5_K_M | 5 | 48.75 GB| 51.25 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## How to run in `llama.cpp` Make sure you are using `llama.cpp` from commit [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa) or earlier. For compatibility with latest llama.cpp, please use GGUF files instead. I use the following command line; adjust for your tastes and needs: ``` ./main -t 10 -ngl 40 -gqa 8 -m platypus2-70b-instruct.ggmlv3.q4_K_M.bin --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. If you are fully offloading the model to GPU, use `-t 1` Change `-ngl 40` to the number of GPU layers you have VRAM for. Use `-ngl 100` to offload all layers to VRAM - if you have a 48GB card, or 2 x 24GB, or similar. Otherwise you can partially offload as many as you have VRAM for, on one or more GPUs. If you want to have a chat-style conversation, replace the `-p ` argument with `-i -ins` Remember the `-gqa 8` argument, required for Llama 70B models. Change `-c 4096` to the desired sequence length for this model. For models that use RoPE, add `--rope-freq-base 10000 --rope-freq-scale 0.5` for doubled context, or `--rope-freq-base 10000 --rope-freq-scale 0.25` for 4x context. For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md). ## 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! 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**: Russ Johnson, J, alfie_i, Alex, NimbleBox.ai, Chadd, Mandus, Nikolai Manek, Ken Nordquist, ya boyyy, Illia Dulskyi, Viktor Bowallius, vamX, Iucharbius, zynix, Magnesian, Clay Pascal, Pierre Kircher, Enrico Ros, Tony Hughes, Elle, Andrey, knownsqashed, Deep Realms, Jerry Meng, Lone Striker, Derek Yates, Pyrater, Mesiah Bishop, James Bentley, Femi Adebogun, Brandon Frisco, SuperWojo, Alps Aficionado, Michael Dempsey, Vitor Caleffi, Will Dee, Edmond Seymore, usrbinkat, LangChain4j, Kacper Wikieł, Luke Pendergrass, John Detwiler, theTransient, Nathan LeClaire, Tiffany J. Kim, biorpg, Eugene Pentland, Stanislav Ovsiannikov, Fred von Graf, terasurfer, Kalila, Dan Guido, Nitin Borwankar, 阿明, Ai Maven, John Villwock, Gabriel Puliatti, Stephen Murray, Asp the Wyvern, danny, Chris Smitley, ReadyPlayerEmma, S_X, Daniel P. Andersen, Olakabola, Jeffrey Morgan, Imad Khwaja, Caitlyn Gatomon, webtim, Alicia Loh, Trenton Dambrowitz, Swaroop Kallakuri, Erik Bjäreholt, Leonard Tan, Spiking Neurons AB, Luke @flexchar, Ajan Kanaga, Thomas Belote, Deo Leter, RoA, Willem Michiel, transmissions 11, subjectnull, Matthew Berman, Joseph William Delisle, David Ziegler, Michael Davis, Johann-Peter Hartmann, Talal Aujan, senxiiz, Artur Olbinski, Rainer Wilmers, Spencer Kim, Fen Risland, Cap'n Zoog, Rishabh Srivastava, Michael Levine, Geoffrey Montalvo, Sean Connelly, Alexandros Triantafyllidis, Pieter, Gabriel Tamborski, Sam, Subspace Studios, Junyu Yang, Pedro Madruga, Vadim, Cory Kujawski, K, Raven Klaugh, Randy H, Mano Prime, Sebastain Graf, Space Cruiser Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. # Original model card: garage-bAInd's Platypus2 70B Instruct # Platypus2-70B-instruct Platypus-70B-instruct is a merge of [`garage-bAInd/Platypus2-70B`](https://huggingface.co/garage-bAInd/Platypus2-70B) and [`upstage/Llama-2-70b-instruct-v2`](https://huggingface.co/upstage/Llama-2-70b-instruct-v2). ![Platty](./Best_Platty_small.jpeg) ### Benchmark Metrics | Metric | Value | |-----------------------|-------| | MMLU (5-shot) | 70.48 | | ARC (25-shot) | 71.84 | | HellaSwag (10-shot) | 87.94 | | TruthfulQA (0-shot) | 62.26 | | Avg. | 73.13 | We use state-of-the-art [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard. Please see below for detailed instructions on reproducing benchmark results. ### Model Details * **Trained by**: **Platypus2-70B** trained by Cole Hunter & Ariel Lee; **Llama-2-70b-instruct** trained by upstageAI * **Model type:** **Platypus2-70B-instruct** is an auto-regressive language model based on the LLaMA 2 transformer architecture. * **Language(s)**: English * **License**: Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/)) ### Prompt Template ``` ### Instruction: (without the <>) ### Response: ``` ### Training Dataset `garage-bAInd/Platypus2-70B` trained using STEM and logic based dataset [`garage-bAInd/Open-Platypus`](https://huggingface.co/datasets/garage-bAInd/Open-Platypus). Please see our [paper](https://arxiv.org/abs/2308.07317) and [project webpage](https://platypus-llm.github.io) for additional information. ### Training Procedure `garage-bAInd/Platypus2-70B` was instruction fine-tuned using LoRA on 8 A100 80GB. For training details and inference instructions please see the [Platypus](https://github.com/arielnlee/Platypus) GitHub repo. ### Reproducing Evaluation Results Install LM Evaluation Harness: ``` # clone repository git clone https://github.com/EleutherAI/lm-evaluation-harness.git # change to repo directory cd lm-evaluation-harness # check out the correct commit git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463 # install pip install -e . ``` Each task was evaluated on a single A100 80GB GPU. ARC: ``` python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-70B-instruct --tasks arc_challenge --batch_size 1 --no_cache --write_out --output_path results/Platypus2-70B-instruct/arc_challenge_25shot.json --device cuda --num_fewshot 25 ``` HellaSwag: ``` python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-70B-instruct --tasks hellaswag --batch_size 1 --no_cache --write_out --output_path results/Platypus2-70B-instruct/hellaswag_10shot.json --device cuda --num_fewshot 10 ``` MMLU: ``` python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-70B-instruct --tasks hendrycksTest-* --batch_size 1 --no_cache --write_out --output_path results/Platypus2-70B-instruct/mmlu_5shot.json --device cuda --num_fewshot 5 ``` TruthfulQA: ``` python main.py --model hf-causal-experimental --model_args pretrained=garage-bAInd/Platypus2-70B-instruct --tasks truthfulqa_mc --batch_size 1 --no_cache --write_out --output_path results/Platypus2-70B-instruct/truthfulqa_0shot.json --device cuda ``` ### Limitations and bias Llama 2 and fine-tuned variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2 and any fine-tuned varient's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2 variants, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/ ### Citations ```bibtex @article{platypus2023, title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs}, author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz}, booktitle={arXiv preprint arxiv:2308.07317}, year={2023} } ``` ```bibtex @misc{touvron2023llama, title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov year={2023}, eprint={2307.09288}, archivePrefix={arXiv}, } ``` ```bibtex @inproceedings{ hu2022lora, title={Lo{RA}: Low-Rank Adaptation of Large Language Models}, author={Edward J Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen}, booktitle={International Conference on Learning Representations}, year={2022}, url={https://openreview.net/forum?id=nZeVKeeFYf9} } ```