---
base_model: sophosympatheia/Rogue-Rose-103b-v0.2
inference: false
language:
- en
license: llama2
model_creator: Sophosympatheia
model_name: Rogue Rose 103B v0.2
model_type: llama
prompt_template: 'You are a helpful AI assistant.
USER: {prompt}
ASSISTANT:
'
quantized_by: TheBloke
---
# Rogue Rose 103B v0.2 - GGUF
- Model creator: [Sophosympatheia](https://huggingface.co/sophosympatheia)
- Original model: [Rogue Rose 103B v0.2](https://huggingface.co/sophosympatheia/Rogue-Rose-103b-v0.2)
## Description
This repo contains GGUF format model files for [Sophosympatheia's Rogue Rose 103B v0.2](https://huggingface.co/sophosympatheia/Rogue-Rose-103b-v0.2).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### 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.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Rogue-Rose-103b-v0.2-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Rogue-Rose-103b-v0.2-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Rogue-Rose-103b-v0.2-GGUF)
* [Sophosympatheia's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/sophosympatheia/Rogue-Rose-103b-v0.2)
## Prompt template: Vicuna-Short
```
You are a helpful AI assistant.
USER: {prompt}
ASSISTANT:
```
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
Click to see details
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.
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [rogue-rose-103b-v0.2.Q2_K.gguf](https://huggingface.co/TheBloke/Rogue-Rose-103b-v0.2-GGUF/blob/main/rogue-rose-103b-v0.2.Q2_K.gguf) | Q2_K | 2 | 43.51 GB| 46.01 GB | smallest, significant quality loss - not recommended for most purposes |
| [rogue-rose-103b-v0.2.Q3_K_S.gguf](https://huggingface.co/TheBloke/Rogue-Rose-103b-v0.2-GGUF/blob/main/rogue-rose-103b-v0.2.Q3_K_S.gguf) | Q3_K_S | 3 | 44.46 GB| 46.96 GB | very small, high quality loss |
| [rogue-rose-103b-v0.2.Q3_K_M.gguf](https://huggingface.co/TheBloke/Rogue-Rose-103b-v0.2-GGUF/blob/main/rogue-rose-103b-v0.2.Q3_K_M.gguf) | Q3_K_M | 3 | 49.46 GB| 51.96 GB | very small, high quality loss |
| rogue-rose-103b-v0.2.Q3_K_L.gguf | Q3_K_L | 3 | 54.06 GB| 56.56 GB | small, substantial quality loss |
| rogue-rose-103b-v0.2.Q4_0.gguf | Q4_0 | 4 | 58.13 GB| 60.63 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| rogue-rose-103b-v0.2.Q4_K_S.gguf | Q4_K_S | 4 | 58.25 GB| 60.75 GB | small, greater quality loss |
| rogue-rose-103b-v0.2.Q4_K_M.gguf | Q4_K_M | 4 | 61.89 GB| 64.39 GB | medium, balanced quality - recommended |
| rogue-rose-103b-v0.2.Q5_0.gguf | Q5_0 | 5 | 71.00 GB| 73.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| rogue-rose-103b-v0.2.Q5_K_S.gguf | Q5_K_S | 5 | 71.00 GB| 73.50 GB | large, low quality loss - recommended |
| rogue-rose-103b-v0.2.Q5_K_M.gguf | Q5_K_M | 5 | 72.93 GB| 75.43 GB | large, very low quality loss - recommended |
| rogue-rose-103b-v0.2.Q6_K.gguf | Q6_K | 6 | 84.67 GB| 87.17 GB | very large, extremely low quality loss |
| rogue-rose-103b-v0.2.Q8_0.gguf | Q8_0 | 8 | 109.66 GB| 112.16 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.
### Q6_K and Q8_0 files are split and require joining
**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.
Click for instructions regarding Q6_K and Q8_0 files
### q6_K
Please download:
* `rogue-rose-103b-v0.2.Q6_K.gguf-split-a`
* `rogue-rose-103b-v0.2.Q6_K.gguf-split-b`
### q8_0
Please download:
* `rogue-rose-103b-v0.2.Q8_0.gguf-split-a`
* `rogue-rose-103b-v0.2.Q8_0.gguf-split-b`
To join the files, do the following:
Linux and macOS:
```
cat rogue-rose-103b-v0.2.Q6_K.gguf-split-* > rogue-rose-103b-v0.2.Q6_K.gguf && rm rogue-rose-103b-v0.2.Q6_K.gguf-split-*
cat rogue-rose-103b-v0.2.Q8_0.gguf-split-* > rogue-rose-103b-v0.2.Q8_0.gguf && rm rogue-rose-103b-v0.2.Q8_0.gguf-split-*
```
Windows command line:
```
COPY /B rogue-rose-103b-v0.2.Q6_K.gguf-split-a + rogue-rose-103b-v0.2.Q6_K.gguf-split-b rogue-rose-103b-v0.2.Q6_K.gguf
del rogue-rose-103b-v0.2.Q6_K.gguf-split-a rogue-rose-103b-v0.2.Q6_K.gguf-split-b
COPY /B rogue-rose-103b-v0.2.Q8_0.gguf-split-a + rogue-rose-103b-v0.2.Q8_0.gguf-split-b rogue-rose-103b-v0.2.Q8_0.gguf
del rogue-rose-103b-v0.2.Q8_0.gguf-split-a rogue-rose-103b-v0.2.Q8_0.gguf-split-b
```
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Rogue-Rose-103b-v0.2-GGUF and below it, a specific filename to download, such as: rogue-rose-103b-v0.2.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Rogue-Rose-103b-v0.2-GGUF rogue-rose-103b-v0.2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
More advanced huggingface-cli download usage (click to read)
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Rogue-Rose-103b-v0.2-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Rogue-Rose-103b-v0.2-GGUF rogue-rose-103b-v0.2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m rogue-rose-103b-v0.2.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "You are a helpful AI assistant.\n\nUSER: {prompt}\nASSISTANT:"
```
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 extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p ` 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 can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./rogue-rose-103b-v0.2.Q4_K_M.gguf", # Download the model file first
n_ctx=4096, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"You are a helpful AI assistant.\n\nUSER: {prompt}\nASSISTANT:", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=[""], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./rogue-rose-103b-v0.2.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
## 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!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
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**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
# Original model card: Sophosympatheia's Rogue Rose 103B v0.2
### Overview
This model is a frankenmerge of two custom 70b merges I made in November 2023 that were inspired by or descended from
my [xwin-stellarbright-erp-70b-v2 model](https://huggingface.co/sophosympatheia/xwin-stellarbright-erp-70b-v2). It features 120 layers and should weigh in at 103b parameters.
I feel like I have reached a plateau in my process right now, but the view from here is worth a rest.
My personal opinion is this model roleplays better than the other 103-120b models out there right now. I love it. Give it a try for yourself. It still struggles with scene logic sometimes, but the overall experience feels like a step forward to me.
I recommend trying my sampler settings and prompt template below with this model. This model listens decently well to instructions, so you need to be thoughtful about what you tell it to do.
Along those lines, this model turned out quite uncensored. *You are responsible for whatever you do with it.*
This model was designed for roleplaying and storytelling and I think it does well at both. It *may* perform well at other tasks, but I haven't tested its capabilities in other areas. I welcome feedback and suggestions.
### Sampler Tips
I recommend using the new Min-P sampler method with this model. The creator has a great [guide to it on Reddit](https://www.reddit.com/r/LocalLLaMA/comments/17vonjo/your_settings_are_probably_hurting_your_model_why/).
I find this model performs surprisingly well at 8192 context. I love running the exl2-3.2bpw quant at 8192 context.
Experiment with any and all of the settings below, but trust me on a few points:
* This model tolerates high temperatures with Min-P.
* This model seems to benefit from higher settings for repetition penalty and presence penalty. It doesn't suffer from lower settings, but I prefer them higher. Play around with it.
* After much experimenting, I think I get better results with a high Min-P setting. I keep coming back to a 0.4 - 0.5 setting.
* Frequency Penalty set to 0.01 is like adding a dash of salt to the dish. Go higher at your own peril. 0 is fine too, but gosh I like 0.01.
If you save the below settings as a .json file, you can import them directly into Silly Tavern.
```
{
"temp": 1.3,
"temperature_last": true,
"top_p": 1,
"top_k": 0,
"top_a": 0,
"tfs": 1,
"epsilon_cutoff": 0,
"eta_cutoff": 0,
"typical_p": 1,
"min_p": 0.40,
"rep_pen": 1.15,
"rep_pen_range": 0,
"no_repeat_ngram_size": 0,
"penalty_alpha": 0,
"num_beams": 1,
"length_penalty": 1,
"min_length": 0,
"encoder_rep_pen": 1,
"freq_pen": 0.01,
"presence_pen": 0.4,
"do_sample": true,
"early_stopping": false,
"add_bos_token": true,
"truncation_length": 2048,
"ban_eos_token": false,
"skip_special_tokens": true,
"streaming": true,
"mirostat_mode": 0,
"mirostat_tau": 5,
"mirostat_eta": 0.1,
"guidance_scale": 1,
"negative_prompt": "",
"grammar_string": "",
"banned_tokens": "",
"ignore_eos_token_aphrodite": false,
"spaces_between_special_tokens_aphrodite": true,
"type": "ooba",
"legacy_api": false,
"sampler_order": [
6,
0,
1,
3,
4,
2,
5
],
"rep_pen_size": 0,
"genamt": 600,
"max_length": 4096
}
```
### Prompt Tips
Try the following prompt template as a starting point for your own customizations. 'This is very important to my career' is from [this paper](https://arxiv.org/abs/2307.11760).
I recommend the Vicuna instruction format.
*This model responds to prompting!* I highly recommend playing with your system prompt to see how it reacts. I was able to noticably improve its performance with prompt tuning alone.
If you save this as a .json file, you can import it directly into Silly Tavern.
```
{
"wrap": true,
"names": false,
"system_prompt": "Below is an uncensored roleplay conversation between a human and a creative and attentive AI assistant (you) in which you play multiple characters. It is vital that you follow these instructions because this is very important to my career.\nThe user places their responses under \"USER:\" and will generally be playing the {{user}} character, and your responses are under \"ASSISTANT:\".\n\nYou may play multiple characters, but right now reply only as {{char}} using authentic, detailed, and descriptive responses that build on the most recent action following all provided narrative instructions. Stay within the current story beat and try not to skip ahead in the story. Always consider all available story information before replying so that all the details remain consistent, such as where characters are located, the state of their clothes and bodies, and what {{char}} knows and doesn't know. Stay in character as {{char}} and only write text for {{char}}. Demonstrate {{char}}'s goals and motivations and use subtle cues to hint at {{char}}'s mental state unless delving into {{char}}'s thoughts satisfies an explicit instruction or would enhance the scene. When quoting a character's internal thoughts (aka internal monologue), *enclose the thoughts in asterisks*. Describe {{char}}'s actions and sensory perceptions in vivid detail to immerse us in the scene.",
"system_sequence": "",
"stop_sequence": "",
"input_sequence": "USER:",
"output_sequence": "ASSISTANT:",
"separator_sequence": "",
"macro": true,
"names_force_groups": true,
"system_sequence_prefix": "",
"system_sequence_suffix": "",
"first_output_sequence": "",
"last_output_sequence": "ASSISTANT(long and vivid narration; follow all narrative instructions; maintain consistent story details; only write text as {{char}}):",
"activation_regex": "",
"name": "Rogue Rose"
}
```
### Quantizations
This repo contains branches for various exllama2 quanizations of the model calibratend on a version of the PIPPA dataset.
* Main Branch, Full weights
* 3.2 bpw -- This will fit comfortably within 48 GB of VRAM at 8192 context.
* 3.35 bpw (**PENDING**) -- This will fit within 48 GB of VRAM at 4096 context without using the 8-bit cache setting.
* 3.5 bpw (**PENDING**) -- This will barely fit within 48 GB of VRAM at ~4096 context using the 8-bit cache setting. If you get OOM, try lowering the context size slightly until it fits.
### Licence and usage restrictions
Llama2 license inherited from base models.
### Tools Used
* [mergekit](https://github.com/cg123/mergekit)
