README.md

---
inference: false
language:
- en
license: llama2
model_creator: ddobokki
model_link: https://huggingface.co/ddobokki/Llama-2-70b-orca-200k
model_name: Llama 2 70B Orca 200k
model_type: llama
pipeline_tag: text-generation
quantized_by: TheBloke
tags:
- llama-2
- instruct
- instruction
---

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# Llama 2 70B Orca 200k - GGUF
- Model creator: [ddobokki](https://huggingface.co/ddobokki)
- Original model: [Llama 2 70B Orca 200k](https://huggingface.co/ddobokki/Llama-2-70b-orca-200k)

## Description

This repo contains GGUF format model files for [ddobokki's Llama 2 70B Orca 200k](https://huggingface.co/ddobokki/Llama-2-70b-orca-200k).

<!-- 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.
* [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:
* [LM Studio](https://lmstudio.ai/), should be updated by end August 25th.
<!-- 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/Llama-2-70B-Orca-200k-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-70B-Orca-200k-GGUF)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/Llama-2-70B-Orca-200k-GGML)
* [ddobokki's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/ddobokki/Llama-2-70b-orca-200k)
<!-- repositories-available end -->

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

```
### Human: {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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [llama-2-70b-orca-200k.Q2_K.gguf](https://huggingface.co/TheBloke/Llama-2-70B-Orca-200k-GGUF/blob/main/llama-2-70b-orca-200k.Q2_K.gguf) | Q2_K | 2 | 29.28 GB| 31.78 GB | smallest, significant quality loss - not recommended for most purposes |
| [llama-2-70b-orca-200k.Q3_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-70B-Orca-200k-GGUF/blob/main/llama-2-70b-orca-200k.Q3_K_S.gguf) | Q3_K_S | 3 | 29.92 GB| 32.42 GB | very small, high quality loss |
| [llama-2-70b-orca-200k.Q3_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-70B-Orca-200k-GGUF/blob/main/llama-2-70b-orca-200k.Q3_K_M.gguf) | Q3_K_M | 3 | 33.19 GB| 35.69 GB | very small, high quality loss |
| [llama-2-70b-orca-200k.Q3_K_L.gguf](https://huggingface.co/TheBloke/Llama-2-70B-Orca-200k-GGUF/blob/main/llama-2-70b-orca-200k.Q3_K_L.gguf) | Q3_K_L | 3 | 36.15 GB| 38.65 GB | small, substantial quality loss |
| [llama-2-70b-orca-200k.Q4_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-70B-Orca-200k-GGUF/blob/main/llama-2-70b-orca-200k.Q4_K_S.gguf) | Q4_K_S | 4 | 39.07 GB| 41.57 GB | small, greater quality loss |
| [llama-2-70b-orca-200k.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-70B-Orca-200k-GGUF/blob/main/llama-2-70b-orca-200k.Q4_K_M.gguf) | Q4_K_M | 4 | 41.42 GB| 43.92 GB | medium, balanced quality - recommended |
| [llama-2-70b-orca-200k.Q5_K_S.gguf](https://huggingface.co/TheBloke/Llama-2-70B-Orca-200k-GGUF/blob/main/llama-2-70b-orca-200k.Q5_K_S.gguf) | Q5_K_S | 5 | 47.46 GB| 49.96 GB | large, low quality loss - recommended |
| [llama-2-70b-orca-200k.Q5_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-70B-Orca-200k-GGUF/blob/main/llama-2-70b-orca-200k.Q5_K_M.gguf) | Q5_K_M | 5 | 48.75 GB| 51.25 GB | large, very low quality loss - recommended |
| llama-2-70b-orca-200k.Q6_K.bin | q6_K | 6 | 56.82 GB | 59.32 GB | very large, extremely low quality loss |
| llama-2-70b-orca-200k.Q8_0.bin | q8_0 | 8 | 73.29 GB | 75.79 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.

<details>
  <summary>Click for instructions regarding Q6_K and Q8_0 files</summary>
   
### q6_K 
Please download:
* `llama-2-70b-orca-200k.Q6_K.gguf-split-a`
* `llama-2-70b-orca-200k.Q6_K.gguf-split-b`

### q8_0
Please download:
* `llama-2-70b-orca-200k.Q8_0.gguf-split-a`
* `llama-2-70b-orca-200k.Q8_0.gguf-split-b`

To join the files, do the following:

Linux and macOS:
```
cat llama-2-70b-orca-200k.Q6_K.gguf-split-* > llama-2-70b-orca-200k.Q6_K.gguf && rm llama-2-70b-orca-200k.Q6_K.gguf-split-*
cat llama-2-70b-orca-200k.Q8_0.gguf-split-* > llama-2-70b-orca-200k.Q8_0.gguf && rm llama-2-70b-orca-200k.Q8_0.gguf-split-*
```
Windows command line:
```
COPY /B llama-2-70b-orca-200k.Q6_K.gguf-split-a + llama-2-70b-orca-200k.Q6_K.gguf-split-b llama-2-70b-orca-200k.Q6_K.gguf
del llama-2-70b-orca-200k.Q6_K.gguf-split-a llama-2-70b-orca-200k.Q6_K.gguf-split-b

COPY /B llama-2-70b-orca-200k.Q8_0.gguf-split-a + llama-2-70b-orca-200k.Q8_0.gguf-split-b llama-2-70b-orca-200k.Q8_0.gguf
del llama-2-70b-orca-200k.Q8_0.gguf-split-a llama-2-70b-orca-200k.Q8_0.gguf-split-b
```

</details>

<!-- 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 llama-2-70b-orca-200k.q4_K_M.gguf --color -c 4096 --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.

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 -->

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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**: 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.

<!-- footer end -->

<!-- original-model-card start -->
# Original model card: ddobokki's Llama 2 70B Orca 200k


# Llama-2-70b-orca-200k model card

### Used Datasets
- OpenOrca (200k sampling)

### Prompt Template
```
### Human: {Human}
### Assistant: {Assistant}
```

### Contribute
[ddobokki](https://github.com/ddobokki)

[YooSungHyun](https://github.com/YooSungHyun)

### License
[LICENSE.txt](meta-license/LICENSE.txt)

### USE_POLICY
[USE_POLICY.md](meta-license/USE_POLICY.md)

### Responsible Use Guide
[Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf)

<!-- original-model-card end -->