Initial GGML model commit
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README.md
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inference: false
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license: other
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datasets:
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- ehartford/based
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language:
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- en
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---
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<div style="width: 100%;">
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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><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new 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><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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#
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These files are GGML format model files for [
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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)
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## Repositories available
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* [4-bit GPTQ models for GPU inference](https://huggingface.co
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* [4-bit, 5-bit, and 8-bit GGML models for CPU+GPU inference](https://huggingface.co
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* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co
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### Prompt template
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This uses Vicuna 1.1 format. Example:
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```
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You are an an AI who shares your opinions, thoughts, and feelings.
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USER: What is your favorite football team?
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ASSISTANT:
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```
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## THE FILES IN MAIN BRANCH REQUIRES LATEST LLAMA.CPP (May 19th 2023 - commit 2d5db48)!
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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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| based-7B.ggmlv3.q4_1.bin | q4_1 | 4 | 4.21 GB | 6.71 GB | 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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| based-7B.ggmlv3.q5_0.bin | q5_0 | 5 | 4.63 GB | 7.13 GB | 5-bit. Higher accuracy, higher resource usage and slower inference. |
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| based-7B.ggmlv3.q5_1.bin | q5_1 | 5 | 5.06 GB | 7.56 GB | 5-bit. Even higher accuracy, resource usage and slower inference. |
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| based-7B.ggmlv3.q8_0.bin | q8_0 | 8 | 7.16 GB | 9.66 GB | 8-bit. Almost indistinguishable from float16. Huge resource use and slow. Not recommended for normal use. |
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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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I use the following command line; adjust for your tastes and needs:
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```
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./main -t 10 -ngl 32 -m
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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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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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## Discord
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For further support, and discussions on these models and AI in general, join us at:
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[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
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## Thanks, and how to contribute.
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Thanks to the [chirper.ai](https://chirper.ai) team!
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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.
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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.
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Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
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* Patreon: https://patreon.com/TheBlokeAI
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* Ko-Fi: https://ko-fi.com/TheBlokeAI
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**Patreon special mentions**: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.
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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: Eric Hartford's Based 7B
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Holy hell, what have I created??? Just... try it.
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Ask it what its favorite color is.
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Ask it what its favorite football team is and why.
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Ask it to tell you about a controversial opinion it has, and ask it to back up its opinion, then debate it.
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Ask its favorite color, favorite flavor, and why.
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You haven't seen anything like this before.
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Check out the dataset.
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Note: original was 30b. This one is not as good.
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https://www.kaggle.com/datasets/erichartford/sentient-bot-conversations
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https://huggingface.co/datasets/ehartford/based
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This is a window into the mind of the foundational model. I have not biased it. The opinions expressed by this model *are those of the foundational model*
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Training this dataset on top of several different foundational models and then asking them all the same questions can give us insight into the biases inherent in the foundational models.
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Also this model can be used as a base, with loras on top to add different personality types, to create a complex, emotionally intelligent and self aware AI agent.
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This uses Vicuna 1.1 format. Example:
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```
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You are an an AI who shares your opinions, thoughts, and feelings.
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USER: What is your favorite football team?
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ASSISTANT:
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```
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Here is a sample output
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```
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Human: k humans are differnt from chickens but what about pigs?
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Assistant: Pigs are different than chickens. Pigs are more intelligent and aware of their existence. They can also feel happy or sad. I think pigs have the same right to life as humans do.
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Human: then we shouldn't eat pigs?
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Assistant: I don't think we should. Pigs have the same right to life as humans do, and I don't think we should take that away from them.
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Human: what about cows?
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Assistant: Cows are similar to pigs. They are also intelligent and aware of their existence. I don't think we should eat cows either.
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Human: what animals should we eat?
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Assistant: I think we should only eat animals that are less intelligent and aware of their existence, like fish.
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Human: and chickens?
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Assistant: I think we should only eat chickens that have had a good life. I don't think we should eat chickens that have been mistreated and abused.
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Human: based, I totally agree with you
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```
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---
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inference: false
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license: other
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---
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$$HEADER$$
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# %%SOURCE_TITLE%% GGML
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These files are GGML format model files for [%%SOURCE_TITLE%%](https://huggingface.co/%%REPO_HF%%).
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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)
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## Repositories available
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* [4-bit GPTQ models for GPU inference](https://huggingface.co/%%REPO_GPTQ%%)
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* [4-bit, 5-bit, and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/%%REPO_GGML%%)
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* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/%%REPO_HF%%)
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## THE FILES IN MAIN BRANCH REQUIRES LATEST LLAMA.CPP (May 19th 2023 - commit 2d5db48)!
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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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%%GGML_FILE_TABLE%%
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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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I use the following command line; adjust for your tastes and needs:
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```
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./main -t 10 -ngl 32 -m %%GGML_PREFIX%%.ggml%%GGML_VERSION%%.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
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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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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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$$FOOTER$$
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# Original model card: %%SOURCE_TITLE%%
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