File size: 14,642 Bytes

---
license: llama2
model_name: Chronos Beluga v2 13B
inference: false
model_creator: Caleb Morgan
model_link: https://huggingface.co/The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16
model_type: llama
quantized_by: TheBloke
base_model: The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16
---

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# Chronos Beluga v2 13B - GGML
- Model creator: [Caleb Morgan](https://huggingface.co/The-Face-Of-Goonery)
- Original model: [Chronos Beluga v2 13B](https://huggingface.co/The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16)

## Description

This repo contains GGML format model files for [Caleb Morgan's Chronos Beluga v2 13B](https://huggingface.co/The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16).

### Important note regarding GGML files.

The GGML format has now been superseded by GGUF. As of August 21st 2023, [llama.cpp](https://github.com/ggerganov/llama.cpp) no longer supports GGML models. Third party clients and libraries are expected to still support it for a time, but many may also drop support.

Please use the GGUF models instead.
### About GGML

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/Chronos-Beluga-v2-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Chronos-Beluga-v2-13B-GGUF)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/Chronos-Beluga-v2-13B-GGML)
* [Caleb Morgan's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16)

## 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

These quantised GGML files are compatible with llama.cpp between June 6th (commit `2d43387`) and August 21st 2023.

For support with latest llama.cpp, please use GGUF files instead.

The final llama.cpp commit with support for GGML was: [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa)

As of August 23rd 2023 they are still compatible with all UIs, libraries and utilities which use GGML. This may change in the future.

## 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [chronos-beluga-v2-13b.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/Chronos-Beluga-v2-13B-GGML/blob/main/chronos-beluga-v2-13b.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. |
| [chronos-beluga-v2-13b.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/Chronos-Beluga-v2-13B-GGML/blob/main/chronos-beluga-v2-13b.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 |
| [chronos-beluga-v2-13b.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/Chronos-Beluga-v2-13B-GGML/blob/main/chronos-beluga-v2-13b.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 |
| [chronos-beluga-v2-13b.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/Chronos-Beluga-v2-13B-GGML/blob/main/chronos-beluga-v2-13b.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 |
| [chronos-beluga-v2-13b.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/Chronos-Beluga-v2-13B-GGML/blob/main/chronos-beluga-v2-13b.ggmlv3.q4_0.bin) | q4_0 | 4 | 7.37 GB| 9.87 GB | Original quant method, 4-bit. |
| [chronos-beluga-v2-13b.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/Chronos-Beluga-v2-13B-GGML/blob/main/chronos-beluga-v2-13b.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 |
| [chronos-beluga-v2-13b.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/Chronos-Beluga-v2-13B-GGML/blob/main/chronos-beluga-v2-13b.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 |
| [chronos-beluga-v2-13b.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/Chronos-Beluga-v2-13B-GGML/blob/main/chronos-beluga-v2-13b.ggmlv3.q4_1.bin) | q4_1 | 4 | 8.17 GB| 10.67 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. |
| [chronos-beluga-v2-13b.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/Chronos-Beluga-v2-13B-GGML/blob/main/chronos-beluga-v2-13b.ggmlv3.q5_0.bin) | q5_0 | 5 | 8.97 GB| 11.47 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| [chronos-beluga-v2-13b.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/Chronos-Beluga-v2-13B-GGML/blob/main/chronos-beluga-v2-13b.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 |
| [chronos-beluga-v2-13b.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/Chronos-Beluga-v2-13B-GGML/blob/main/chronos-beluga-v2-13b.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 |
| [chronos-beluga-v2-13b.ggmlv3.q5_1.bin](https://huggingface.co/TheBloke/Chronos-Beluga-v2-13B-GGML/blob/main/chronos-beluga-v2-13b.ggmlv3.q5_1.bin) | q5_1 | 5 | 9.78 GB| 12.28 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| [chronos-beluga-v2-13b.ggmlv3.q6_K.bin](https://huggingface.co/TheBloke/Chronos-Beluga-v2-13B-GGML/blob/main/chronos-beluga-v2-13b.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 |
| [chronos-beluga-v2-13b.ggmlv3.q8_0.bin](https://huggingface.co/TheBloke/Chronos-Beluga-v2-13B-GGML/blob/main/chronos-beluga-v2-13b.ggmlv3.q8_0.bin) | q8_0 | 8 | 13.79 GB| 16.29 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`

Make sure you are using `llama.cpp` from commit [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa) or earlier.

For compatibility with latest llama.cpp, please use GGUF files instead.

```
./main -t 10 -ngl 32 -m chronos-beluga-v2-13b.ggmlv3.q4_K_M.bin --color -c 2048 --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:\nWrite a story about llamas\n\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).

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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.

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Thank you to all my generous patrons and donaters!

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# Original model card: Caleb Morgan's Chronos Beluga v2 13B

merged 58% chronos v2 42% beluga 13b merge using LUNK(Large universal neural kombiner)

https://huggingface.co/The-Face-Of-Goonery/Beluga-Hermes_LLama-2_13b

https://huggingface.co/elinas/chronos-13b-v2/tree/main