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--- |
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license: other |
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inference: false |
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--- |
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# WizardLM: An Instruction-following LLM Using Evol-Instruct |
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These files are the result of merging the [delta weights](https://huggingface.co./victor123/WizardLM) with the original Llama7B model. |
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The code for merging is provided in the [WizardLM official Github repo](https://github.com/nlpxucan/WizardLM). |
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## WizardLM-7B GGML |
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This repo contains GGML files for for CPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp). |
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## Other repositories available |
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* [4bit GPTQ models for GPU inference](https://huggingface.co./TheBloke/wizardLM-7B-GPTQ) |
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* [Unquantised model in HF format](https://huggingface.co./TheBloke/wizardLM-7B-HF) |
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## THE FILES IN MAIN BRANCH REQUIRES LATEST LLAMA.CPP (May 19th 2023 - commit 2d5db48)! |
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llama.cpp recently made another breaking change to its quantisation methods - https://github.com/ggerganov/llama.cpp/pull/1508 |
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I have quantised the GGML files in this repo with the latest version. Therefore you will require llama.cpp compiled on May 19th or later (commit `2d5db48` or later) to use them. |
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For files compatible with the previous version of llama.cpp, please see branch `previous_llama_ggmlv2`. |
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## Provided files |
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| Name | Quant method | Bits | Size | RAM required | Use case | |
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| ---- | ---- | ---- | ---- | ---- | ----- | |
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`WizardLM-7B.GGML.q4_0.bin` | q4_0 | 4bit | 4.2GB | 6GB | 4bit. | |
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`WizardLM-7B.GGML.q4_1.bin` | q4_0 | 4bit | 4.63GB | 6GB | 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-7B.GGML.q5_0.bin` | q5_0 | 5bit | 4.63GB | 7GB | 5-bit. Higher accuracy, higher resource usage and slower inference.| |
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`WizardLM-7B.GGML.q5_1.bin` | q5_1 | 5bit | 5.0GB | 7GB | 5-bit. Even higher accuracy, and higher resource usage and slower inference. | |
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`WizardLM-7B.GGML.q8_0.bin` | q8_0 | 8bit | 8GB | 10GB | 8-bit. Almost indistinguishable from float16. Huge resource use and slow. Not recommended for normal use. | |
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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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``` |
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./main -t 18 -m WizardLM-7B.GGML.q4_0.bin --color -c 2048 --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. |
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### Instruction: |
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Write a story about llamas |
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### Response:" |
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``` |
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Change `-t 18` 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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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-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md). |
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Note: at this time text-generation-webui may not support the new May 19th llama.cpp quantisation methods for q4_0, q4_1 and q8_0 files. |
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# Original model info |
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Overview of Evol-Instruct |
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Evol-Instruct is a novel method using LLMs instead of humans to automatically mass-produce open-domain instructions of various difficulty levels and skills range, to improve the performance of LLMs. |
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