README.md

---
datasets:
- Open-Orca/OpenOrca
- anon8231489123/ShareGPT_Vicuna_unfiltered
- jondurbin/airoboros-uncensored
inference: false
language:
- en
license: other
metrics:
- accuracy
model_type: llama
pipeline_tag: text-generation
tags:
- llama
- alpaca
- vicuna
- uncensored
- merge
- mix
- airoboros
- openorca
- orcamini
- orca
- instruct
- mixtune
---

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# CalderaAI's 13B Ouroboros GGML

These files are GGML format model files for [CalderaAI's 13B Ouroboros](https://huggingface.co/CalderaAI/13B-Ouroboros).

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:
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with full GPU acceleration out of the box. Especially good for story telling.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with GPU acceleration via the c_transformers backend.
* [LM Studio](https://lmstudio.ai/), a fully featured local GUI. Supports full GPU accel on macOS. Also supports Windows, without GPU accel.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Requires extra steps to enable GPU accel via llama.cpp backend.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with LangChain support and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with OpenAI-compatible API server.


## Repositories available

* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/13B-Ouroboros-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/13B-Ouroboros-GGML)
* [Original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/CalderaAI/13B-Ouroboros)

## Prompt template: Alpaca

```
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction: {prompt}

### Response:
```

<!-- compatibility_ggml start -->
## Compatibility

### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`

These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods.

### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`

These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`.

They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation.

## 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| 13b-ouroboros.ggmlv3.q2_K.bin | q2_K | 2 | 5.51 GB| 8.01 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. |
| 13b-ouroboros.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 6.93 GB| 9.43 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 |
| 13b-ouroboros.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 6.31 GB| 8.81 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 |
| 13b-ouroboros.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 5.66 GB| 8.16 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| 13b-ouroboros.ggmlv3.q4_0.bin | q4_0 | 4 | 7.32 GB| 9.82 GB | Original quant method, 4-bit. |
| 13b-ouroboros.ggmlv3.q4_1.bin | q4_1 | 4 | 8.14 GB| 10.64 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. |
| 13b-ouroboros.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 7.87 GB| 10.37 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 |
| 13b-ouroboros.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 7.37 GB| 9.87 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| 13b-ouroboros.ggmlv3.q5_0.bin | q5_0 | 5 | 8.95 GB| 11.45 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| 13b-ouroboros.ggmlv3.q5_1.bin | q5_1 | 5 | 9.76 GB| 12.26 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| 13b-ouroboros.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 9.23 GB| 11.73 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 |
| 13b-ouroboros.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 8.97 GB| 11.47 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| 13b-ouroboros.ggmlv3.q6_K.bin | q6_K | 6 | 10.68 GB| 13.18 GB | New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization |
| 13b-ouroboros.ggmlv3.q8_0.bin | q8_0 | 8 | 13.83 GB| 16.33 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 13b-ouroboros.ggmlv3.q4_0.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.

If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`

## How to run in `text-generation-webui`

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

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
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**Special thanks to**: Luke from CarbonQuill, Aemon Algiz.

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Thank you to all my generous patrons and donaters!

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# Original model card: CalderaAI's 13B Ouroboros


## 13B-Ouroboros
Ouroboros is an experimental model based on Meta's LLaMA [v1] 13B base model using a custom merging technique, tweaking
each layer's merge % based on internal tests against the PTB dataset, scoring ~26.31 according to internal evaluation
(6 samples, sequence length 1024; this testing is not empirical, it's a quick way to find near-optimum values). Testing,
evaluating, and remixing this model is absolutely permissible and even encouraged (within the bounds of Meta's LLaMAv1
license agreement); the more feedback the better we can tune our process! 😊

## Composition:
Ouroboros is comprised of 40 layers [LLaMAv1 13B standard] mixed at optimized
ratios VS the PTB dataset for lowest perplexity score. Listed below are the
paired models and ratios merged per layer.

Tier One Merge:

13B-airoboros-gpt4-1.4 > 13B-orca_mini_v2

[0.22, 0.85, 0.89, 0.98, 0.3, 0.41, 0.71, 0.83, 0.32, 0.1, 0.44, 0.6, 0.53, 0.15, 0.86, 0.79, 0.93, 0.02, 0.19, 0.82, 0.01, 0.52, 0.07, 0.27, 0.73, 0.86, 0.08, 0.67, 0.42, 0.28, 0.37, 0.08, 0.95, 0.68, 0.45, 0.08, 0.7, 0.93, 0.96, 0.43]

13B-gpt4-x-alpaca > 13B-Vicuna-cocktail

[0.65, 0.94, 0.98, 0.87, 0.28, 0.64, 0.73, 0.7, 0.95, 0.89, 0.84, 0.9, 0.59, 0.92, 0.28, 0.61, 0.88, 0.73, 0.34, 0.85, 0.98, 0.05, 0.74, 0.92, 0.5, 0.78, 0.26, 0.4, 0.27, 0.65, 0.71, 0.7, 0.8, 0.93, 0.36, 0.03, 0.45, 0.39, 0.77, 0.06]

Tier Two Merge:

[13B-airoboros-gpt4-1.4 + 13B-orca_mini_v2] offspring > [13B-gpt4-x-alpaca + 13B-Vicuna-cocktail] offspring

[0.2, 0.83, 0.24, 0.03, 0.37, 0.62, 0.02, 0.82, 0.65, 0.63, 0.45, 0.65, 0.48, 0.45, 0.24, 0.76, 0.06, 0.31, 0.45, 0.86, 0.23, 0.99, 0.93, 0.84, 0.96, 0.53, 0.95, 0.32, 0.19, 0.06, 0.4, 0.08, 0.62, 0.4, 0.26, 0.12, 0.16, 0.91, 0.14, 0.0]

Result:

13B-Ouroboros, a model that seems uncensored and highly competent. So far only Alpaca instruction promting has been tested and seems to work solidly well.

## Use:

Alpaca's instruct format can be used to do many things, including control of the terms of behavior
between a user and a response from an agent in chat. Below is an example of a command injected into
memory.

```
### Instruction:
Make Narrator function as a text based adventure game that responds with verbose, detailed, and creative descriptions of what happens next after Player's response.
Make Player function as the player input for Narrator's text based adventure game, controlling a character named (insert character name here, their short bio, and
whatever quest or other information to keep consistent in the interaction).

### Response:
{an empty new line here}
```

## Language Models Used Credits:

13B-airoboros-gpt4-1.4 by jondurbin

https://huggingface.co/jondurbin/airoboros-13b-gpt4-1.4

13B-orca_mini_v2 by psmathur

https://huggingface.co/psmathur/orca_mini_v2_13b

13B-gpt4-x-alpaca by chavinlo

https://huggingface.co/chavinlo/gpt4-x-alpaca

13B-Vicuna-cocktail by reeducator

https://huggingface.co/reeducator/vicuna-13b-cocktail

Also thanks to Meta for LLaMA.

Each model and LoRA was hand picked and considered for what it could contribute to this ensemble.
Thanks to each and every one of you for your incredible work developing some of the best things
to come out of this community.