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README.md
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inference: false
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language:
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license:
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model_creator: garage-bAInd
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model_link: https://huggingface.co/garage-bAInd/Camel-Platypus2-70B
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model_name: Camel Platypus2 70B
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---
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<!-- header start -->
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</div>
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<div style="display: flex; justify-content: space-between; width: 100%;">
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
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<p><a href="https://discord.gg/theblokeai">Chat & support:
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</div>
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<div style="display: flex; flex-direction: column; align-items: flex-end;">
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<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
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</div>
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</div>
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<!-- header end -->
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# Camel Platypus2 70B - GGML
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This repo contains GGML format model files for [garage-bAInd's Camel Platypus2 70B](https://huggingface.co/garage-bAInd/Camel-Platypus2-70B).
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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:
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* [llama.cpp](https://github.com/ggerganov/llama.cpp), commit `e76d630` and later.
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* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI.
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## Repositories available
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* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GPTQ)
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* [2, 3, 4, 5, 6 and 8-bit
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* [garage-bAInd's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/garage-bAInd/Camel-Platypus2-70B)
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## Prompt template: Alpaca
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{prompt}
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### Response:
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```
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<!-- compatibility_ggml start -->
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## Compatibility
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###
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Or one of the other tools and libraries listed above.
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| Name | Quant method | Bits | Size | Max RAM required | Use case |
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| ---- | ---- | ---- | ---- | ---- | ----- |
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| [camel-platypus2-70b.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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. |
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| [camel-platypus2-70b.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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 |
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| [camel-platypus2-70b.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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 |
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| [camel-platypus2-70b.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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 |
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| [camel-platypus2-70b.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.ggmlv3.q4_0.bin) | q4_0 | 4 | 38.87 GB| 41.37 GB | Original quant method, 4-bit. |
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| [camel-platypus2-70b.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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. |
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| [camel-platypus2-70b.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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 |
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| [camel-platypus2-70b.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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 |
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| [camel-platypus2-70b.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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. |
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| [camel-platypus2-70b.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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 |
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| [camel-platypus2-70b.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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 |
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| camel-platypus2-70b.ggmlv3.
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| camel-platypus2-70b.ggmlv3.q6_K.bin | q6_K | 6 | 56.59 GB | 59.09 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
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| camel-platypus2-70b.ggmlv3.q8_0.bin | q8_0 | 8 | 73.23 GB | 75.73 GB | Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
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**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.
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<summary>Click for instructions regarding q5_1, q6_K and q8_0 files</summary>
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### q5_1
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Please download:
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* `camel-platypus2-70b.ggmlv3.q5_1.zip`
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* `camel-platypus2-70b.ggmlv3.q5_1.z01`
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### q6_K
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Please download:
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* `camel-platypus2-70b.ggmlv3.q6_K.zip`
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* `camel-platypus2-70b.ggmlv3.q6_K.z01`
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### q8_0
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Please download:
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* `camel-platypus2-70b.ggmlv3.q8_0.zip`
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* `camel-platypus2-70b.ggmlv3.q8_0.z01`
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Then extract the .zip archive. This will will expand both parts automatically. On Linux I found I had to use `7zip` - the basic `unzip` tool did not work. Example:
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```
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sudo apt update -y && sudo apt install 7zip
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7zz x camel-platypus2-70b.ggmlv3.q6_K.zip
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```
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</details>
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## How to run in `llama.cpp`
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I use the following command line; adjust for your tastes and needs:
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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).
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<!-- footer start -->
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## Discord
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For further support, and discussions on these models and AI in general, join us at:
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* Patreon: https://patreon.com/TheBlokeAI
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* Ko-Fi: https://ko-fi.com/TheBlokeAI
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**Special thanks to**:
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**Patreon special mentions**:
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Thank you to all my generous patrons and donaters!
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<!-- footer end -->
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# Original model card: garage-bAInd's Camel Platypus2 70B
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# Platypus2-70B
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Camel-Platypus2-70B is a merge of [`garage-bAInd/Platypus2-70B`](https://huggingface.co/garage-bAInd/Platypus2-70B) and [`augtoma/qCammel-70-x`](https://huggingface.co/augtoma/qCammel-70-x).
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| Metric | Value |
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|-----------------------|-------|
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| MMLU (5-shot) |
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| ARC (25-shot) |
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| HellaSwag (10-shot) |
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| TruthfulQA (0-shot) |
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| Avg. |
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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.
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### Training Dataset
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`garage-bAInd/Platypus2-70B` trained using STEM and logic based dataset [`garage-bAInd/Open-Platypus`](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
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### Training Procedure
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Please see the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/
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### Citations
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```bibtex
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@misc{touvron2023llama,
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title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
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author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov
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}
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```
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```bibtex
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}
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```
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inference: false
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language:
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- en
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license: llama2
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model_creator: garage-bAInd
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model_link: https://huggingface.co/garage-bAInd/Camel-Platypus2-70B
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model_name: Camel Platypus2 70B
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---
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<!-- header start -->
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<!-- 200823 -->
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<div style="width: auto; margin-left: auto; margin-right: auto">
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<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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</div>
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<div style="display: flex; justify-content: space-between; width: 100%;">
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
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</div>
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<div style="display: flex; flex-direction: column; align-items: flex-end;">
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
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</div>
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</div>
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
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<!-- header end -->
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# Camel Platypus2 70B - GGML
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This repo contains GGML format model files for [garage-bAInd's Camel Platypus2 70B](https://huggingface.co/garage-bAInd/Camel-Platypus2-70B).
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### Important note regarding GGML files.
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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.
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Please use the GGUF models instead.
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### About GGML
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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:
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* [llama.cpp](https://github.com/ggerganov/llama.cpp), commit `e76d630` and later.
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* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI.
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## Repositories available
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* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GPTQ)
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* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGUF)
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* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML)
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* [garage-bAInd's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/garage-bAInd/Camel-Platypus2-70B)
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## Prompt template: Alpaca
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{prompt}
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### Response:
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```
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<!-- compatibility_ggml start -->
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## Compatibility
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### Works with llama.cpp [commit `e76d630`](https://github.com/ggerganov/llama.cpp/commit/e76d630df17e235e6b9ef416c45996765d2e36fb) until August 21st, 2023
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Will not work with `llama.cpp` after commit [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa).
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For compatibility with latest llama.cpp, please use GGUF files instead.
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Or one of the other tools and libraries listed above.
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| Name | Quant method | Bits | Size | Max RAM required | Use case |
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| ---- | ---- | ---- | ---- | ---- | ----- |
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| [camel-platypus2-70b.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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. |
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| [camel-platypus2-70b.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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 |
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| [camel-platypus2-70b.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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 |
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| [camel-platypus2-70b.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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 |
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| [camel-platypus2-70b.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.ggmlv3.q4_0.bin) | q4_0 | 4 | 38.87 GB| 41.37 GB | Original quant method, 4-bit. |
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| [camel-platypus2-70b.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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 |
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| [camel-platypus2-70b.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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 |
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| [camel-platypus2-70b.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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. |
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| [camel-platypus2-70b.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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. |
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| [camel-platypus2-70b.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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 |
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| [camel-platypus2-70b.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/Camel-Platypus2-70B-GGML/blob/main/camel-platypus2-70b.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 |
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**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.
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## How to run in `llama.cpp`
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Make sure you are using `llama.cpp` from commit [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa) or earlier.
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For compatibility with latest llama.cpp, please use GGUF files instead.
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I use the following command line; adjust for your tastes and needs:
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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).
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<!-- footer start -->
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<!-- 200823 -->
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## Discord
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For further support, and discussions on these models and AI in general, join us at:
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* Patreon: https://patreon.com/TheBlokeAI
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* Ko-Fi: https://ko-fi.com/TheBlokeAI
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**Special thanks to**: Aemon Algiz.
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**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
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Thank you to all my generous patrons and donaters!
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And thank you again to a16z for their generous grant.
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<!-- footer end -->
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# Original model card: garage-bAInd's Camel Platypus2 70B
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# Camel-Platypus2-70B
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Camel-Platypus2-70B is a merge of [`garage-bAInd/Platypus2-70B`](https://huggingface.co/garage-bAInd/Platypus2-70B) and [`augtoma/qCammel-70-x`](https://huggingface.co/augtoma/qCammel-70-x).
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| Metric | Value |
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|-----------------------|-------|
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| MMLU (5-shot) | 69.80 |
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| ARC (25-shot) | 71.16 |
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| HellaSwag (10-shot) | 87.66 |
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| TruthfulQA (0-shot) | 57.77 |
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| Avg. | 71.60 |
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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.
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### Training Dataset
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`garage-bAInd/Platypus2-70B` trained using STEM and logic based dataset [`garage-bAInd/Open-Platypus`](https://huggingface.co/datasets/garage-bAInd/Open-Platypus).
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Please see our [paper](https://arxiv.org/abs/2308.07317) and [project webpage](https://platypus-llm.github.io) for additional information.
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### Training Procedure
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Please see the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/
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### Citations
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```bibtex
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@article{platypus2023,
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title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs},
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author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz},
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booktitle={arXiv preprint arxiv:2308.07317},
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year={2023}
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}
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```
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```bibtex
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@misc{touvron2023llama,
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title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
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author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov year={2023},
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eprint={2307.09288},
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archivePrefix={arXiv},
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}
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```
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```bibtex
|
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+
@inproceedings{
|
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hu2022lora,
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title={Lo{RA}: Low-Rank Adaptation of Large Language Models},
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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},
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booktitle={International Conference on Learning Representations},
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year={2022},
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url={https://openreview.net/forum?id=nZeVKeeFYf9}
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}
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```
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