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README.md
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inference: false
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license:
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model_creator: WizardLM
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model_link: https://huggingface.co/WizardLM/WizardLM-13B-V1.2
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model_name: WizardLM 13B V1.2
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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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# WizardLM 13B V1.2 - GGML
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This repo contains GGML format model files for [WizardLM's WizardLM 13B V1.2](https://huggingface.co/WizardLM/WizardLM-13B-V1.2).
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GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
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* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Supports NVidia CUDA GPU acceleration.
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* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with GPU acceleration on all platforms (CUDA and OpenCL). Especially good for story telling.
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## Repositories available
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* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GPTQ)
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* [2, 3, 4, 5, 6 and 8-bit
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* [WizardLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/WizardLM/WizardLM-13B-V1.2)
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## Prompt template: Vicuna
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```
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A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
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USER: {prompt}
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ASSISTANT:
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```
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<!-- compatibility_ggml start -->
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## Compatibility
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They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation.
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## Explanation of the new k-quant methods
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<details>
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<!-- compatibility_ggml end -->
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## Provided files
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| Name | Quant method | Bits | Size | Max RAM required | Use case |
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| [wizardlm-13b-v1.2.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q2_K.bin) | q2_K | 2 | 5.51 GB| 8.01 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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| [wizardlm-13b-v1.2.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q3_K_L.bin) | q3_K_L | 3 | 6.93 GB| 9.43 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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| [wizardlm-13b-v1.2.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q3_K_M.bin) | q3_K_M | 3 | 6.31 GB| 8.81 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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| [wizardlm-13b-v1.2.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q3_K_S.bin) | q3_K_S | 3 | 5.66 GB| 8.16 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
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| [wizardlm-13b-v1.2.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q4_0.bin) | q4_0 | 4 | 7.32 GB| 9.82 GB | Original quant method, 4-bit. |
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| [wizardlm-13b-v1.2.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q4_1.bin) | q4_1 | 4 | 8.14 GB| 10.64 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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| [wizardlm-13b-v1.2.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q4_K_M.bin) | q4_K_M | 4 | 7.87 GB| 10.37 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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| [wizardlm-13b-v1.2.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q4_K_S.bin) | q4_K_S | 4 | 7.37 GB| 9.87 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
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| [wizardlm-13b-v1.2.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q5_0.bin) | q5_0 | 5 | 8.95 GB| 11.45 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
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| [wizardlm-13b-v1.2.ggmlv3.q5_1.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q5_1.bin) | q5_1 | 5 | 9.76 GB| 12.26 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
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| [wizardlm-13b-v1.2.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q5_K_M.bin) | q5_K_M | 5 | 9.23 GB| 11.73 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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| [wizardlm-13b-v1.2.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q5_K_S.bin) | q5_K_S | 5 | 8.97 GB| 11.47 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
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| [wizardlm-13b-v1.2.ggmlv3.q6_K.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q6_K.bin) | q6_K | 6 | 10.68 GB| 13.18 GB | New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization |
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| [wizardlm-13b-v1.2.ggmlv3.q8_0.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q8_0.bin) | q8_0 | 8 | 13.83 GB| 16.33 GB | Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
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## How to run in `llama.cpp`
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```
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./main -t 10 -ngl 32 -m wizardlm-13b-v1.2.ggmlv3.
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```
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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`.
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Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
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If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
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## How to run in `text-generation-webui`
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Further instructions here: [text-generation-webui/docs/llama.cpp
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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: WizardLM's WizardLM 13B V1.2
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This is the **Full-Weight** of WizardLM-13B V1.2 model
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**Repository**: https://github.com/nlpxucan/WizardLM
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- π₯π₯π₯ [7/25/2023] We released **WizardLM V1.2** models. The **WizardLM-13B-V1.2** is here ([Demo_13B-V1.2](https://b7a19878988c8c73.gradio.app), [Demo_13B-V1.2_bak-1](https://d0a37a76e0ac4b52.gradio.app/), [Full Model Weight](https://huggingface.co/WizardLM/WizardLM-13B-V1.2)). Please checkout the [paper](https://arxiv.org/abs/2304.12244).
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- π₯π₯π₯ [7/25/2023] The **WizardLM-13B-V1.2** achieves **7.06** on [MT-Bench Leaderboard](https://chat.lmsys.org/?leaderboard), **89.17%** on [AlpacaEval Leaderboard](https://tatsu-lab.github.io/alpaca_eval/), and **101.4%** on [WizardLM Eval](https://github.com/nlpxucan/WizardLM/blob/main/WizardLM/data/WizardLM_testset.jsonl). (Note: MT-Bench and AlpacaEval are all self-test, will push update and request review. All tests are completed under their official settings.)
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---
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inference: false
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license: llama2
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model_creator: WizardLM
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model_link: https://huggingface.co/WizardLM/WizardLM-13B-V1.2
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model_name: WizardLM 13B V1.2
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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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# WizardLM 13B V1.2 - GGML
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This repo contains GGML format model files for [WizardLM's WizardLM 13B V1.2](https://huggingface.co/WizardLM/WizardLM-13B-V1.2).
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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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GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
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* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Supports NVidia CUDA GPU acceleration.
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* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with GPU acceleration on all platforms (CUDA and OpenCL). Especially good for story telling.
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## Repositories available
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* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GPTQ)
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* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGUF)
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* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML)
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* [WizardLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/WizardLM/WizardLM-13B-V1.2)
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## Prompt template: Vicuna
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```
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A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT:
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```
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<!-- compatibility_ggml start -->
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## Compatibility
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These quantised GGML files are compatible with llama.cpp between June 6th (commit `2d43387`) and August 21st 2023.
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For support with latest llama.cpp, please use GGUF files instead.
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The final llama.cpp commit with support for GGML was: [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa)
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As of August 23rd 2023 they are still compatible with all UIs, libraries and utilities which use GGML. This may change in the future.
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## Explanation of the new k-quant methods
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<details>
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<!-- compatibility_ggml end -->
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## Provided files
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| Name | Quant method | Bits | Size | Max RAM required | Use case |
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| ---- | ---- | ---- | ---- | ---- | ----- |
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| [wizardlm-13b-v1.2.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q2_K.bin) | q2_K | 2 | 5.51 GB| 8.01 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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| [wizardlm-13b-v1.2.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q3_K_S.bin) | q3_K_S | 3 | 5.66 GB| 8.16 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
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| [wizardlm-13b-v1.2.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q3_K_M.bin) | q3_K_M | 3 | 6.31 GB| 8.81 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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| [wizardlm-13b-v1.2.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q3_K_L.bin) | q3_K_L | 3 | 6.93 GB| 9.43 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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| [wizardlm-13b-v1.2.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q4_0.bin) | q4_0 | 4 | 7.32 GB| 9.82 GB | Original quant method, 4-bit. |
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| [wizardlm-13b-v1.2.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q4_K_S.bin) | q4_K_S | 4 | 7.37 GB| 9.87 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
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| [wizardlm-13b-v1.2.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q4_K_M.bin) | q4_K_M | 4 | 7.87 GB| 10.37 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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| [wizardlm-13b-v1.2.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q4_1.bin) | q4_1 | 4 | 8.14 GB| 10.64 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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| [wizardlm-13b-v1.2.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q5_0.bin) | q5_0 | 5 | 8.95 GB| 11.45 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
|
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| [wizardlm-13b-v1.2.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q5_K_S.bin) | q5_K_S | 5 | 8.97 GB| 11.47 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
|
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| [wizardlm-13b-v1.2.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q5_K_M.bin) | q5_K_M | 5 | 9.23 GB| 11.73 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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| [wizardlm-13b-v1.2.ggmlv3.q5_1.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q5_1.bin) | q5_1 | 5 | 9.76 GB| 12.26 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
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| [wizardlm-13b-v1.2.ggmlv3.q6_K.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q6_K.bin) | q6_K | 6 | 10.68 GB| 13.18 GB | New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization |
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| [wizardlm-13b-v1.2.ggmlv3.q8_0.bin](https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGML/blob/main/wizardlm-13b-v1.2.ggmlv3.q8_0.bin) | q8_0 | 8 | 13.83 GB| 16.33 GB | Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
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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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```
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./main -t 10 -ngl 32 -m wizardlm-13b-v1.2.ggmlv3.q4_K_M.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Write a story about llamas ASSISTANT:"
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```
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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`.
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Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
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Change `-c 2048` to the desired sequence length for this model. For example, `-c 4096` for a Llama 2 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.
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+
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If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
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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)
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## How to run in `text-generation-webui`
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Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.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 WikieΕ, Luke Pendergrass, John Detwiler, theTransient, Nathan LeClaire, Tiffany J. Kim, biorpg, Eugene Pentland, Stanislav Ovsiannikov, Fred von Graf, terasurfer, Kalila, Dan Guido, Nitin Borwankar, ιΏζ, 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 BjΓ€reholt, 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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+
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<!-- footer end -->
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# Original model card: WizardLM's WizardLM 13B V1.2
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This is the **Full-Weight** of WizardLM-13B V1.2 model, this model is trained from **Llama-2 13b**.
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+
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## WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions
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<p align="center">
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π€ <a href="https://huggingface.co/WizardLM" target="_blank">HF Repo</a> β’π± <a href="https://github.com/nlpxucan/WizardLM" target="_blank">Github Repo</a> β’ π¦ <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> β’ π <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> β’ π <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> β’ π <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br>
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</p>
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<p align="center">
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π Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a>
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+
</p>
|
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+
|
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## News
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+
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- π₯π₯π₯[2023/08/26] We released **WizardCoder-Python-34B-V1.0** , which achieves the **73.2 pass@1** and surpasses **GPT4 (2023/03/15)**, **ChatGPT-3.5**, and **Claude2** on the [HumanEval Benchmarks](https://github.com/openai/human-eval). For more details, please refer to [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder).
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- [2023/06/16] We released **WizardCoder-15B-V1.0** , which surpasses **Claude-Plus (+6.8)**, **Bard (+15.3)** and **InstructCodeT5+ (+22.3)** on the [HumanEval Benchmarks](https://github.com/openai/human-eval). For more details, please refer to [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder).
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| Model | Checkpoint | Paper | HumanEval | MBPP | Demo | License |
|
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| ----- |------| ---- |------|-------| ----- | ----- |
|
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| WizardCoder-Python-34B-V1.0 | π€ <a href="https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0" target="_blank">HF Link</a> | π <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 73.2 | 61.2 | [Demo](http://47.103.63.15:50085/) | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> |
|
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| WizardCoder-15B-V1.0 | π€ <a href="https://huggingface.co/WizardLM/WizardCoder-15B-V1.0" target="_blank">HF Link</a> | π <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 59.8 |50.6 | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> |
|
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+
| WizardCoder-Python-13B-V1.0 | π€ <a href="https://huggingface.co/WizardLM/WizardCoder-Python-13B-V1.0" target="_blank">HF Link</a> | π <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 64.0 | 55.6 | -- | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> |
|
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+
| WizardCoder-Python-7B-V1.0 | π€ <a href="https://huggingface.co/WizardLM/WizardCoder-Python-7B-V1.0" target="_blank">HF Link</a> | π <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 55.5 | 51.6 | [Demo](http://47.103.63.15:50088/) | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> |
|
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+
| WizardCoder-3B-V1.0 | π€ <a href="https://huggingface.co/WizardLM/WizardCoder-3B-V1.0" target="_blank">HF Link</a> | π <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 34.8 |37.4 | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> |
|
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+
| WizardCoder-1B-V1.0 | π€ <a href="https://huggingface.co/WizardLM/WizardCoder-1B-V1.0" target="_blank">HF Link</a> | π <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 23.8 |28.6 | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> |
|
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+
|
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+
|
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+
- π₯ [08/11/2023] We release **WizardMath** Models.
|
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+
- π₯ Our **WizardMath-70B-V1.0** model slightly outperforms some closed-source LLMs on the GSM8K, including **ChatGPT 3.5**, **Claude Instant 1** and **PaLM 2 540B**.
|
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+
- π₯ Our **WizardMath-70B-V1.0** model achieves **81.6 pass@1** on the [GSM8k Benchmarks](https://github.com/openai/grade-school-math), which is **24.8** points higher than the SOTA open-source LLM.
|
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+
- π₯ Our **WizardMath-70B-V1.0** model achieves **22.7 pass@1** on the [MATH Benchmarks](https://github.com/hendrycks/math), which is **9.2** points higher than the SOTA open-source LLM.
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+
|
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+
|
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+
| Model | Checkpoint | Paper | GSM8k | MATH |Online Demo| License|
|
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+
| ----- |------| ---- |------|-------| ----- | ----- |
|
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+
| WizardMath-70B-V1.0 | π€ <a href="https://huggingface.co/WizardLM/WizardMath-70B-V1.0" target="_blank">HF Link</a> | π <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **81.6** | **22.7** |[Demo](http://47.103.63.15:50083/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> |
|
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+
| WizardMath-13B-V1.0 | π€ <a href="https://huggingface.co/WizardLM/WizardMath-13B-V1.0" target="_blank">HF Link</a> | π <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **63.9** | **14.0** |[Demo](http://47.103.63.15:50082/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> |
|
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+
| WizardMath-7B-V1.0 | π€ <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.0" target="_blank">HF Link</a> | π <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **54.9** | **10.7** | [Demo](http://47.103.63.15:50080/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a>|
|
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+
|
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+
|
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+
<font size=4>
|
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+
|
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+
| <sup>Model</sup> | <sup>Checkpoint</sup> | <sup>Paper</sup> |<sup>MT-Bench</sup> | <sup>AlpacaEval</sup> | <sup>WizardEval</sup> | <sup>HumanEval</sup> | <sup>License</sup>|
|
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+
| ----- |------| ---- |------|-------| ----- | ----- | ----- |
|
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+
| <sup>WizardLM-13B-V1.2</sup> | <sup>π€ <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.2" target="_blank">HF Link</a> </sup>| | <sup>7.06</sup> | <sup>89.17%</sup> | <sup>101.4% </sup>|<sup>36.6 pass@1</sup>|<sup> <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a></sup> |
|
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+
| <sup>WizardLM-13B-V1.1</sup> |<sup> π€ <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.1" target="_blank">HF Link</a> </sup> | | <sup>6.76</sup> |<sup>86.32%</sup> | <sup>99.3% </sup> |<sup>25.0 pass@1</sup>| <sup>Non-commercial</sup>|
|
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+
| <sup>WizardLM-30B-V1.0</sup> | <sup>π€ <a href="https://huggingface.co/WizardLM/WizardLM-30B-V1.0" target="_blank">HF Link</a></sup> | | <sup>7.01</sup> | | <sup>97.8% </sup> | <sup>37.8 pass@1</sup>| <sup>Non-commercial</sup> |
|
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+
| <sup>WizardLM-13B-V1.0</sup> | <sup>π€ <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.0" target="_blank">HF Link</a> </sup> | | <sup>6.35</sup> | <sup>75.31%</sup> | <sup>89.1% </sup> |<sup> 24.0 pass@1 </sup> | <sup>Non-commercial</sup>|
|
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+
| <sup>WizardLM-7B-V1.0 </sup>| <sup>π€ <a href="https://huggingface.co/WizardLM/WizardLM-7B-V1.0" target="_blank">HF Link</a> </sup> |<sup> π <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> </sup>| | | <sup>78.0% </sup> |<sup>19.1 pass@1 </sup>|<sup> Non-commercial</sup>|
|
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+
</font>
|
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|
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**Repository**: https://github.com/nlpxucan/WizardLM
|
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|
|
|
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|
227 |
- π₯π₯π₯ [7/25/2023] We released **WizardLM V1.2** models. The **WizardLM-13B-V1.2** is here ([Demo_13B-V1.2](https://b7a19878988c8c73.gradio.app), [Demo_13B-V1.2_bak-1](https://d0a37a76e0ac4b52.gradio.app/), [Full Model Weight](https://huggingface.co/WizardLM/WizardLM-13B-V1.2)). Please checkout the [paper](https://arxiv.org/abs/2304.12244).
|
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- π₯π₯π₯ [7/25/2023] The **WizardLM-13B-V1.2** achieves **7.06** on [MT-Bench Leaderboard](https://chat.lmsys.org/?leaderboard), **89.17%** on [AlpacaEval Leaderboard](https://tatsu-lab.github.io/alpaca_eval/), and **101.4%** on [WizardLM Eval](https://github.com/nlpxucan/WizardLM/blob/main/WizardLM/data/WizardLM_testset.jsonl). (Note: MT-Bench and AlpacaEval are all self-test, will push update and request review. All tests are completed under their official settings.)
|
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+
|
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+
β<b>Note for model system prompts usage:</b>
|
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+
|
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+
|
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+
<b>WizardLM</b> adopts the prompt format from <b>Vicuna</b> and supports **multi-turn** conversation. The prompt should be as following:
|
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+
|
235 |
+
```
|
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+
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s>USER: Who are you? ASSISTANT: I am WizardLM.</s>......
|
237 |
+
```
|
238 |
+
|
239 |
+
## Inference WizardLM Demo Script
|
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+
|
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+
We provide the inference WizardLM demo code [here](https://github.com/nlpxucan/WizardLM/tree/main/demo).
|
242 |
+
|
243 |
+
β<b>To commen concern about dataset:</b>
|
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+
|
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+
Recently, there have been clear changes in the open-source policy and regulations of our overall organization's code, data, and models.
|
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+
|
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+
|
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+
Despite this, we have still worked hard to obtain opening the weights of the model first, but the data involves stricter auditing and is in review with our legal team .
|
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+
|
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+
Our researchers have no authority to publicly release them without authorization.
|
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+
|
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Thank you for your understanding.
|