Initial GGML model commit

README.md

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+---
+inference: false
+license: other
+model_creator: Nick Perez
+model_link: https://huggingface.co/nkpz/llama2-22b-daydreamer-v2
+model_name: Llama2 22B Daydreamer v2
+model_type: llama
+quantized_by: TheBloke
+---
+
+<!-- header start -->
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+<!-- header end -->
+
+# Llama2 22B Daydreamer v2 - GGML
+- Model creator: [Nick Perez](https://huggingface.co/nkpz)
+- Original model: [Llama2 22B Daydreamer v2](https://huggingface.co/nkpz/llama2-22b-daydreamer-v2)
+
+## Description
+
+This repo contains GGML format model files for [Nick Perez's Llama2 22B Daydreamer v2](https://huggingface.co/nkpz/llama2-22b-daydreamer-v2).
+
+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:
+* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Supports NVidia CUDA GPU acceleration.
+* [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.
+* [LM Studio](https://lmstudio.ai/), a fully featured local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS.
+* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with CUDA GPU acceleration via the c_transformers backend.
+* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
+* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
+
+## Repositories available
+
+* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/llama2-22B-daydreamer-v2-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/llama2-22B-daydreamer-v2-GGML)
+* [Nick Perez's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/nkpz/llama2-22b-daydreamer-v2)
+
+## Prompt template: Custom
+
+Q&A Example
+
+```
+Question: {prompt}
+Answer:
+``
+
+
+An example of how it handles different roles, which I still like to use explicit instructions for:
+
+````
+### Instruction
+Complete the story in a manner that accurately reflects the scenario summary.
+
+### Scenario: 
+A hot dog salesman at a baseball game is annoyed and behaving rudely because I don't want to buy a hot dog.
+
+### Begin Chat
+Hot Dog Salesman:
+```
+
+
+<!-- compatibility_ggml start -->
+## Compatibility
+
+These quantised GGML files are compatible with llama.cpp as of June 6th, commit `2d43387`.
+
+They should also be compatible with all UIs, libraries and utilities which use GGML.
+
+## Explanation of the new k-quant methods
+<details>
+  <summary>Click to see details</summary>
+
+The new methods available are:
+* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
+* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
+* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
+* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
+* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
+* GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.
+
+Refer to the Provided Files table below to see what files use which methods, and how.
+</details>
+<!-- compatibility_ggml end -->
+
+## Provided files
+
+| Name | Quant method | Bits | Size | Max RAM required | Use case |
+| ---- | ---- | ---- | ---- | ---- | ----- |
+| [llama2-22b-daydreamer-v2.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/llama2-22B-daydreamer-v2-GGML/blob/main/llama2-22b-daydreamer-v2.ggmlv3.q2_K.bin) | q2_K | 2 | 9.22 GB| 11.72 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. |
+| [llama2-22b-daydreamer-v2.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/llama2-22B-daydreamer-v2-GGML/blob/main/llama2-22b-daydreamer-v2.ggmlv3.q3_K_L.bin) | q3_K_L | 3 | 6.41 GB| 8.91 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 |
+| [llama2-22b-daydreamer-v2.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/llama2-22B-daydreamer-v2-GGML/blob/main/llama2-22b-daydreamer-v2.ggmlv3.q3_K_M.bin) | q3_K_M | 3 | 10.57 GB| 13.07 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 |
+| [llama2-22b-daydreamer-v2.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/llama2-22B-daydreamer-v2-GGML/blob/main/llama2-22b-daydreamer-v2.ggmlv3.q3_K_S.bin) | q3_K_S | 3 | 9.46 GB| 11.96 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
+| [llama2-22b-daydreamer-v2.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/llama2-22B-daydreamer-v2-GGML/blob/main/llama2-22b-daydreamer-v2.ggmlv3.q4_0.bin) | q4_0 | 4 | 12.34 GB| 14.84 GB | Original quant method, 4-bit. |
+| [llama2-22b-daydreamer-v2.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/llama2-22B-daydreamer-v2-GGML/blob/main/llama2-22b-daydreamer-v2.ggmlv3.q4_1.bin) | q4_1 | 4 | 13.69 GB| 16.19 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. |
+| [llama2-22b-daydreamer-v2.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/llama2-22B-daydreamer-v2-GGML/blob/main/llama2-22b-daydreamer-v2.ggmlv3.q4_K_M.bin) | q4_K_M | 4 | 13.18 GB| 15.68 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 |
+| [llama2-22b-daydreamer-v2.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/llama2-22B-daydreamer-v2-GGML/blob/main/llama2-22b-daydreamer-v2.ggmlv3.q4_K_S.bin) | q4_K_S | 4 | 12.34 GB| 14.84 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
+| [llama2-22b-daydreamer-v2.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/llama2-22B-daydreamer-v2-GGML/blob/main/llama2-22b-daydreamer-v2.ggmlv3.q5_0.bin) | q5_0 | 5 | 15.04 GB| 17.54 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
+| [llama2-22b-daydreamer-v2.ggmlv3.q5_1.bin](https://huggingface.co/TheBloke/llama2-22B-daydreamer-v2-GGML/blob/main/llama2-22b-daydreamer-v2.ggmlv3.q5_1.bin) | q5_1 | 5 | 16.39 GB| 18.89 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
+| [llama2-22b-daydreamer-v2.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/llama2-22B-daydreamer-v2-GGML/blob/main/llama2-22b-daydreamer-v2.ggmlv3.q5_K_M.bin) | q5_K_M | 5 | 15.47 GB| 17.97 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 |
+| [llama2-22b-daydreamer-v2.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/llama2-22B-daydreamer-v2-GGML/blob/main/llama2-22b-daydreamer-v2.ggmlv3.q5_K_S.bin) | q5_K_S | 5 | 15.04 GB| 17.54 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
+| [llama2-22b-daydreamer-v2.ggmlv3.q6_K.bin](https://huggingface.co/TheBloke/llama2-22B-daydreamer-v2-GGML/blob/main/llama2-22b-daydreamer-v2.ggmlv3.q6_K.bin) | q6_K | 6 | 17.91 GB| 20.41 GB | New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization |
+| [llama2-22b-daydreamer-v2.ggmlv3.q8_0.bin](https://huggingface.co/TheBloke/llama2-22B-daydreamer-v2-GGML/blob/main/llama2-22b-daydreamer-v2.ggmlv3.q8_0.bin) | q8_0 | 8 | 23.14 GB| 25.64 GB | Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
+
+**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
+
+## How to run in `llama.cpp`
+
+I use the following command line; adjust for your tastes and needs:
+
+```
+./main -t 10 -ngl 32 -m llama2-22b-daydreamer-v2.ggmlv3.q4_K_M.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
+```
+Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.
+
+Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
+
+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.
+
+If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
+
+For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
+
+## How to run in `text-generation-webui`
+
+Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
+
+<!-- footer start -->
+## Discord
+
+For further support, and discussions on these models and AI in general, join us at:
+
+[TheBloke AI's Discord server](https://discord.gg/theblokeai)
+
+## Thanks, and how to contribute.
+
+Thanks to the [chirper.ai](https://chirper.ai) team!
+
+I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
+
+If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
+
+Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
+
+* Patreon: https://patreon.com/TheBlokeAI
+* Ko-Fi: https://ko-fi.com/TheBlokeAI
+
+**Special thanks to**: Luke from CarbonQuill, Aemon Algiz.
+
+**Patreon special mentions**: Ajan Kanaga, David Ziegler, Raymond Fosdick, SuperWojo, Sam, webtim, Steven Wood, knownsqashed, Tony Hughes, Junyu Yang, J, Olakabola, Dan Guido, Stephen Murray, John Villwock, vamX, William Sang, Sean Connelly, LangChain4j, Olusegun Samson, Fen Risland, Derek Yates, Karl Bernard, transmissions 11, Trenton Dambrowitz, Pieter, Preetika Verma, Swaroop Kallakuri, Andrey, Slarti, Jonathan Leane, Michael Levine, Kalila, Joseph William Delisle, Rishabh Srivastava, Deo Leter, Luke Pendergrass, Spencer Kim, Geoffrey Montalvo, Thomas Belote, Jeffrey Morgan, Mandus, ya boyyy, Matthew Berman, Magnesian, Ai Maven, senxiiz, Alps Aficionado, Luke @flexchar, Raven Klaugh, Imad Khwaja, Gabriel Puliatti, Johann-Peter Hartmann, usrbinkat, Spiking Neurons AB, Artur Olbinski, chris gileta, danny, Willem Michiel, WelcomeToTheClub, Deep Realms, alfie_i, Dave, Leonard Tan, NimbleBox.ai, Randy H, Daniel P. Andersen, Pyrater, Will Dee, Elle, Space Cruiser, Gabriel Tamborski, Asp the Wyvern, Illia Dulskyi, Nikolai Manek, Sid, Brandon Frisco, Nathan LeClaire, Edmond Seymore, Enrico Ros, Pedro Madruga, Eugene Pentland, John Detwiler, Mano Prime, Stanislav Ovsiannikov, Alex, Vitor Caleffi, K, biorpg, Michael Davis, Lone Striker, Pierre Kircher, theTransient, Fred von Graf, Sebastain Graf, Vadim, Iucharbius, Clay Pascal, Chadd, Mesiah Bishop, terasurfer, Rainer Wilmers, Alexandros Triantafyllidis, Stefan Sabev, Talal Aujan, Cory Kujawski, Viktor Bowallius, subjectnull, ReadyPlayerEmma, zynix
+
+
+Thank you to all my generous patrons and donaters!
+
+<!-- footer end -->
+
+# Original model card: Nick Perez's Llama2 22B Daydreamer v2
+
+Continuation of https://huggingface.co/nkpz/llama2-22b-daydreamer-v1
+
+Trained it a couple more epochs on airoboros v2 general data.
+
+This resulted in improvements to its obsession with overexplaining itself - it's a little less instruction focused and feels more versatile. Still needs work overall, but I think it's moving in the right direction.
+
+Still more to come. Going to try to find a way to improve its problem solving skills without ruining the output style.