TheBloke
/

Kimiko-v2-13B-GGUF

+---
+inference: false
+language:
+- en
+license: llama2
+model_creator: nRuaif
+model_link: https://huggingface.co/nRuaif/Kimiko-v2-13B
+model_name: Kimiko v2 13B
+model_type: llama
+pipeline_tag: text-generation
+quantized_by: TheBloke
+---
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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>
+<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
+<!-- header end -->
+# Kimiko v2 13B - GGUF
+- Model creator: [nRuaif](https://huggingface.co/nRuaif)
+- Original model: [Kimiko v2 13B](https://huggingface.co/nRuaif/Kimiko-v2-13B)
+## Description
+This repo contains GGUF format model files for [nRuaif's Kimiko v2 13B](https://huggingface.co/nRuaif/Kimiko-v2-13B).
+<!-- README_GGUF.md-about-gguf start -->
+### About GGUF
+GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
+The key benefit of GGUF is that it is a extensible, future-proof format which stores more information about the model as metadata. It also includes significantly improved tokenization code, including for the first time full support for special tokens. This should improve performance, especially with models that use new special tokens and implement custom prompt templates.
+As of August 25th, here is a list of clients and libraries that are known to support GGUF:
+* [llama.cpp](https://github.com/ggerganov/llama.cpp).
+* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI. Supports GGUF with GPU acceleration via the ctransformers backend - llama-cpp-python backend should work soon too.
+* [KoboldCpp](https://github.com/LostRuins/koboldcpp), now supports GGUF as of release 1.41! A powerful GGML web UI, with full GPU accel. Especially good for story telling.
+* [LM Studio](https://lmstudio.ai/), version 0.2.2 and later support GGUF. 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), should now work, choose the `c_transformers` backend. A great web UI with many interesting features. Supports CUDA GPU acceleration.
+* [ctransformers](https://github.com/marella/ctransformers), now supports GGUF as of version 0.2.24! A Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
+* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), supports GGUF as of version 0.1.79. A Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
+* [candle](https://github.com/huggingface/candle), added GGUF support on August 22nd. Candle is a Rust ML framework with a focus on performance, including GPU support, and ease of use.
+The clients and libraries below are expecting to add GGUF support shortly:
+<!-- README_GGUF.md-about-gguf end -->
+<!-- repositories-available start -->
+## Repositories available
+* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Kimiko-v2-13B-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Kimiko-v2-13B-GGUF)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/Kimiko-v2-13B-GGML)
+* [nRuaif's original LoRA adapter, which can be merged on to the base model.](https://huggingface.co/nRuaif/Kimiko-v2-13B)
+<!-- repositories-available end -->
+<!-- prompt-template start -->
+## Prompt template: Vicuna
+```
+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:
+```
+<!-- prompt-template end -->
+<!-- compatibility_gguf start -->
+## Compatibility
+These quantised GGUF files are compatible with llama.cpp from August 21st 2023 onwards, as of commit [6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9](https://github.com/ggerganov/llama.cpp/commit/6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9)
+As of August 24th 2023 they are now compatible with KoboldCpp, release 1.41 and later.
+They are are not yet compatible with any other third-party UIS, libraries or utilities but this is expected to change very soon.
+## Explanation of quantisation 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
+Refer to the Provided Files table below to see what files use which methods, and how.
+</details>
+<!-- compatibility_gguf end -->
+<!-- README_GGUF.md-provided-files start -->
+## Provided files
+| Name | Quant method | Bits | Size | Max RAM required | Use case |
+| ---- | ---- | ---- | ---- | ---- | ----- |
+| [kimiko-v2-13b.Q2_K.gguf](https://huggingface.co/TheBloke/Kimiko-v2-13B-GGUF/blob/main/kimiko-v2-13b.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
+| [kimiko-v2-13b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Kimiko-v2-13B-GGUF/blob/main/kimiko-v2-13b.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
+| [kimiko-v2-13b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Kimiko-v2-13B-GGUF/blob/main/kimiko-v2-13b.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
+| [kimiko-v2-13b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Kimiko-v2-13B-GGUF/blob/main/kimiko-v2-13b.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
+| [kimiko-v2-13b.Q4_0.gguf](https://huggingface.co/TheBloke/Kimiko-v2-13B-GGUF/blob/main/kimiko-v2-13b.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [kimiko-v2-13b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Kimiko-v2-13B-GGUF/blob/main/kimiko-v2-13b.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
+| [kimiko-v2-13b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Kimiko-v2-13B-GGUF/blob/main/kimiko-v2-13b.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
+| [kimiko-v2-13b.Q5_0.gguf](https://huggingface.co/TheBloke/Kimiko-v2-13B-GGUF/blob/main/kimiko-v2-13b.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [kimiko-v2-13b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Kimiko-v2-13B-GGUF/blob/main/kimiko-v2-13b.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
+| [kimiko-v2-13b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Kimiko-v2-13B-GGUF/blob/main/kimiko-v2-13b.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
+| [kimiko-v2-13b.Q6_K.gguf](https://huggingface.co/TheBloke/Kimiko-v2-13B-GGUF/blob/main/kimiko-v2-13b.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
+| [kimiko-v2-13b.Q8_0.gguf](https://huggingface.co/TheBloke/Kimiko-v2-13B-GGUF/blob/main/kimiko-v2-13b.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |
+**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.
+<!-- README_GGUF.md-provided-files end -->
+<!-- README_GGUF.md-how-to-run start -->
+## How to run in `llama.cpp`
+Make sure you are using `llama.cpp` from commit [6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9](https://github.com/ggerganov/llama.cpp/commit/6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9) or later.
+For compatibility with older versions of llama.cpp, or for use with third-party clients and libaries, please use GGML files instead.
+```
+./main -t 10 -ngl 32 -m kimiko-v2-13b.q4_K_M.gguf --color -c 4096 --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:"
+```
+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 4096` to the desired sequence length for this model. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
+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).
+<!-- README_GGUF.md-how-to-run end -->
+<!-- footer start -->
+<!-- 200823 -->
+## 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**: Aemon Algiz.
+**Patreon special mentions**: Kacper Wikieł, knownsqashed, Leonard Tan, Asp the Wyvern, Daniel P. Andersen, Luke Pendergrass, Stanislav Ovsiannikov, RoA, Dave, Ai Maven, Kalila, Will Dee, Imad Khwaja, Nitin Borwankar, Joseph William Delisle, Tony Hughes, Cory Kujawski, Rishabh Srivastava, Russ Johnson, Stephen Murray, Lone Striker, Johann-Peter Hartmann, Elle, J, Deep Realms, SuperWojo, Raven Klaugh, Sebastain Graf, ReadyPlayerEmma, Alps Aficionado, Mano Prime, Derek Yates, Gabriel Puliatti, Mesiah Bishop, Magnesian, Sean Connelly, biorpg, Iucharbius, Olakabola, Fen Risland, Space Cruiser, theTransient, Illia Dulskyi, Thomas Belote, Spencer Kim, Pieter, John Detwiler, Fred von Graf, Michael Davis, Swaroop Kallakuri, subjectnull, Clay Pascal, Subspace Studios, Chris Smitley, Enrico Ros, usrbinkat, Steven Wood, alfie_i, David Ziegler, Willem Michiel, Matthew Berman, Andrey, Pyrater, Jeffrey Morgan, vamX, LangChain4j, Luke @flexchar, Trenton Dambrowitz, Pierre Kircher, Alex, Sam, James Bentley, Edmond Seymore, Eugene Pentland, Pedro Madruga, Rainer Wilmers, Dan Guido, Nathan LeClaire, Spiking Neurons AB, Talal Aujan, zynix, Artur Olbinski, Michael Levine, 阿明, K, John Villwock, Nikolai Manek, Femi Adebogun, senxiiz, Deo Leter, NimbleBox.ai, Viktor Bowallius, Geoffrey Montalvo, Mandus, Ajan Kanaga, ya boyyy, Jonathan Leane, webtim, Brandon Frisco, danny, Alexandros Triantafyllidis, Gabriel Tamborski, Randy H, terasurfer, Vadim, Junyu Yang, Vitor Caleffi, Chadd, transmissions 11
+Thank you to all my generous patrons and donaters!
+And thank you again to a16z for their generous grant.
+<!-- footer end -->
+<!-- original-model-card start -->
+# Original model card: nRuaif's Kimiko v2 13B
+## Model Details
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+### Model Description
+<!-- Provide a longer summary of what this model is. -->
+- **Developed by:** nRuaif
+- **Model type:** large language model
+- **License:**
+- **Finetuned from model [optional]:** Llama-13B
+### Model Sources [optional]
+## Uses
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+The model uses Fastchat/ShareGPT format.
+### Direct Use
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+This model is finetuned for normal and erotic roleplay while can still an assistant. (Might not be a helpfull one through)
+### Out-of-Scope Use
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+Do anything you want. I don't care
+## Bias, Risks, and Limitations
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+Model might have bias to NSFW due to the large % of NSFW data in the training set.
+## Training Details
+### Training Data
+<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+3000 convos with 4090 cut off len.
+### Training Procedure
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+#### Training Hyperparameters
+- **Training regime:** BF16, QLoRA, constant LR 5e-5 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+### Compute Infrastructure
+The model is trained on 1 A100 for 2 hours on runpod.
+<!-- original-model-card end -->