README.md

---
license: llama2
model_name: COTHuginn 4.5 19B
inference: false
model_creator: Caleb Morgan
model_link: https://huggingface.co/The-Face-Of-Goonery/COTHuginn-4.5-19b
model_type: llama
quantized_by: TheBloke
base_model: The-Face-Of-Goonery/COTHuginn-4.5-19b
---

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# COTHuginn 4.5 19B - GGUF
- Model creator: [Caleb Morgan](https://huggingface.co/The-Face-Of-Goonery)
- Original model: [COTHuginn 4.5 19B](https://huggingface.co/The-Face-Of-Goonery/COTHuginn-4.5-19b)

## Description

This repo contains GGUF format model files for [Caleb Morgan's COTHuginn 4.5 19B](https://huggingface.co/The-Face-Of-Goonery/COTHuginn-4.5-19b).

<!-- 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. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.

Here is an incomplate 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.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [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.
* [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.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.

<!-- README_GGUF.md-about-gguf end -->
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## Repositories available

* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/COTHuginn-4.5-19B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/COTHuginn-4.5-19B-GGUF)
* [Caleb Morgan's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/The-Face-Of-Goonery/COTHuginn-4.5-19b)
<!-- repositories-available end -->

<!-- prompt-template start -->
## Prompt template: Alpaca

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

### Instruction:
{prompt}

### Response:

```

<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility

These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](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
<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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [cothuginn-4.5-19b.Q2_K.gguf](https://huggingface.co/TheBloke/COTHuginn-4.5-19B-GGUF/blob/main/cothuginn-4.5-19b.Q2_K.gguf) | Q2_K | 2 | 8.05 GB| 10.55 GB | smallest, significant quality loss - not recommended for most purposes |
| [cothuginn-4.5-19b.Q3_K_S.gguf](https://huggingface.co/TheBloke/COTHuginn-4.5-19B-GGUF/blob/main/cothuginn-4.5-19b.Q3_K_S.gguf) | Q3_K_S | 3 | 8.39 GB| 10.89 GB | very small, high quality loss |
| [cothuginn-4.5-19b.Q3_K_M.gguf](https://huggingface.co/TheBloke/COTHuginn-4.5-19B-GGUF/blob/main/cothuginn-4.5-19b.Q3_K_M.gguf) | Q3_K_M | 3 | 9.39 GB| 11.89 GB | very small, high quality loss |
| [cothuginn-4.5-19b.Q3_K_L.gguf](https://huggingface.co/TheBloke/COTHuginn-4.5-19B-GGUF/blob/main/cothuginn-4.5-19b.Q3_K_L.gguf) | Q3_K_L | 3 | 10.29 GB| 12.79 GB | small, substantial quality loss |
| [cothuginn-4.5-19b.Q4_0.gguf](https://huggingface.co/TheBloke/COTHuginn-4.5-19B-GGUF/blob/main/cothuginn-4.5-19b.Q4_0.gguf) | Q4_0 | 4 | 10.94 GB| 13.44 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [cothuginn-4.5-19b.Q4_K_S.gguf](https://huggingface.co/TheBloke/COTHuginn-4.5-19B-GGUF/blob/main/cothuginn-4.5-19b.Q4_K_S.gguf) | Q4_K_S | 4 | 10.98 GB| 13.48 GB | small, greater quality loss |
| [cothuginn-4.5-19b.Q4_K_M.gguf](https://huggingface.co/TheBloke/COTHuginn-4.5-19B-GGUF/blob/main/cothuginn-4.5-19b.Q4_K_M.gguf) | Q4_K_M | 4 | 11.69 GB| 14.19 GB | medium, balanced quality - recommended |
| [cothuginn-4.5-19b.Q5_0.gguf](https://huggingface.co/TheBloke/COTHuginn-4.5-19B-GGUF/blob/main/cothuginn-4.5-19b.Q5_0.gguf) | Q5_0 | 5 | 13.33 GB| 15.83 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [cothuginn-4.5-19b.Q5_K_S.gguf](https://huggingface.co/TheBloke/COTHuginn-4.5-19B-GGUF/blob/main/cothuginn-4.5-19b.Q5_K_S.gguf) | Q5_K_S | 5 | 13.33 GB| 15.83 GB | large, low quality loss - recommended |
| [cothuginn-4.5-19b.Q5_K_M.gguf](https://huggingface.co/TheBloke/COTHuginn-4.5-19B-GGUF/blob/main/cothuginn-4.5-19b.Q5_K_M.gguf) | Q5_K_M | 5 | 13.72 GB| 16.22 GB | large, very low quality loss - recommended |
| [cothuginn-4.5-19b.Q6_K.gguf](https://huggingface.co/TheBloke/COTHuginn-4.5-19B-GGUF/blob/main/cothuginn-4.5-19b.Q6_K.gguf) | Q6_K | 6 | 15.88 GB| 18.38 GB | very large, extremely low quality loss |
| [cothuginn-4.5-19b.Q8_0.gguf](https://huggingface.co/TheBloke/COTHuginn-4.5-19B-GGUF/blob/main/cothuginn-4.5-19b.Q8_0.gguf) | Q8_0 | 8 | 20.57 GB| 23.07 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 -->
## Example `llama.cpp` command

Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.

```shell
./main -ngl 32 -m cothuginn-4.5-19b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```

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.

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).

## 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.

### How to load this model from Python using ctransformers

#### First install the package

```bash
# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
```

#### Simple example code to load one of these GGUF models

```python
from ctransformers import AutoModelForCausalLM

# 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 = AutoModelForCausalLM.from_pretrained("TheBloke/COTHuginn-4.5-19B-GGUF", model_file="cothuginn-4.5-19b.q4_K_M.gguf", model_type="llama", gpu_layers=50)

print(llm("AI is going to"))
```

## How to use with LangChain

Here's guides on using llama-cpp-python or 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)

<!-- README_GGUF.md-how-to-run end -->

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For further support, and discussions on these models and AI in general, join us at:

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## 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.

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**Special thanks to**: Aemon Algiz.

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Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

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# Original model card: Caleb Morgan's COTHuginn 4.5 19B

I took huginn 4.5, kept the first 40 layers, then added a copy of layers 1-20 on top of it


somehow it works? It's also way better at math now somehow.

I'm posting it on HF so I can try and get it properly evaluated

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