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OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML

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text-generation-inference
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OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML
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---
datasets:
- shahules786/orca-chat
- rombodawg/MegaCodeTraining112k
- theblackcat102/evol-codealpaca-v1
- nickrosh/Evol-Instruct-Code-80k-v1
inference: false
license: other
model_creator: OpenAssistant
model_link: https://huggingface.co/OpenAssistant/llama2-13b-orca-v2-8k-3166
model_name: Llama2 13B Orca v2 8K
model_type: llama
quantized_by: TheBloke
---

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        <p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p>
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        <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
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<!-- header end -->

# Llama2 13B Orca v2 8K - GGML
- Model creator: [OpenAssistant](https://huggingface.co/OpenAssistant)
- Original model: [Llama2 13B Orca v2 8K](https://huggingface.co/OpenAssistant/llama2-13b-orca-v2-8k-3166)

## Description

This repo contains GGML format model files for [OpenAssistant's Llama2 13B Orca v2 8K](https://huggingface.co/OpenAssistant/llama2-13b-orca-v2-8k-3166).

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/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML)
* [OpenAssistant's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/OpenAssistant/llama2-13b-orca-v2-8k-3166)

## Prompt template: OpenAssistant

```
<|prompter|>{prompt}<|endoftext|><|assistant|>
```

<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML/blob/main/openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q2_K.bin) | q2_K | 2 | 5.74 GB| 8.24 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. |
| [openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML/blob/main/openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q3_K_L.bin) | q3_K_L | 3 | 7.14 GB| 9.64 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 |
| [openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML/blob/main/openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q3_K_M.bin) | q3_K_M | 3 | 6.53 GB| 9.03 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 |
| [openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML/blob/main/openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q3_K_S.bin) | q3_K_S | 3 | 5.87 GB| 8.37 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| [openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML/blob/main/openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q4_0.bin) | q4_0 | 4 | 7.32 GB| 9.82 GB | Original quant method, 4-bit. |
| [openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML/blob/main/openassistant-llama2-13b-orca-v2-8k-3166.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. |
| [openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML/blob/main/openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q4_K_M.bin) | q4_K_M | 4 | 8.06 GB| 10.56 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 |
| [openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML/blob/main/openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q4_K_S.bin) | q4_K_S | 4 | 7.56 GB| 10.06 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| [openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML/blob/main/openassistant-llama2-13b-orca-v2-8k-3166.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. |
| [openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q5_1.bin](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML/blob/main/openassistant-llama2-13b-orca-v2-8k-3166.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. |
| [openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML/blob/main/openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q5_K_M.bin) | q5_K_M | 5 | 9.40 GB| 11.90 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 |
| [openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML/blob/main/openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q5_K_S.bin) | q5_K_S | 5 | 9.15 GB| 11.65 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| [openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q6_K.bin](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML/blob/main/openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q6_K.bin) | q6_K | 6 | 10.83 GB| 13.33 GB | New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization |
| [openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q8_0.bin](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-v2-8K-3166-GGML/blob/main/openassistant-llama2-13b-orca-v2-8k-3166.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 openassistant-llama2-13b-orca-v2-8k-3166.ggmlv3.q4_K_M.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).

<!-- footer start -->
## Discord

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!

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**: Luke from CarbonQuill, Aemon Algiz.

**Patreon special mentions**: Willem Michiel, Ajan Kanaga, Cory Kujawski, Alps Aficionado, Nikolai Manek, Jonathan Leane, Stanislav Ovsiannikov, Michael Levine, Luke Pendergrass, Sid, K, Gabriel Tamborski, Clay Pascal, Kalila, William Sang, Will Dee, Pieter, Nathan LeClaire, ya boyyy, David Flickinger, vamX, Derek Yates, Fen Risland, Jeffrey Morgan, webtim, Daniel P. Andersen, Chadd, Edmond Seymore, Pyrater, Olusegun Samson, Lone Striker, biorpg, alfie_i, Mano Prime, Chris Smitley, Dave, zynix, Trenton Dambrowitz, Johann-Peter Hartmann, Magnesian, Spencer Kim, John Detwiler, Iucharbius, Gabriel Puliatti, LangChain4j, Luke @flexchar, Vadim, Rishabh Srivastava, Preetika Verma, Ai Maven, Femi Adebogun, WelcomeToTheClub, Leonard Tan, Imad Khwaja, Steven Wood, Stefan Sabev, Sebastain Graf, usrbinkat, Dan Guido, Sam, Eugene Pentland, Mandus, transmissions 11, Slarti, Karl Bernard, Spiking Neurons AB, Artur Olbinski, Joseph William Delisle, ReadyPlayerEmma, Olakabola, Asp the Wyvern, Space Cruiser, Matthew Berman, Randy H, subjectnull, danny, John Villwock, Illia Dulskyi, Rainer Wilmers, theTransient, Pierre Kircher, Alexandros Triantafyllidis, Viktor Bowallius, terasurfer, Deep Realms, SuperWojo, senxiiz, Oscar Rangel, Alex, Stephen Murray, Talal Aujan, Raven Klaugh, Sean Connelly, Raymond Fosdick, Fred von Graf, chris gileta, Junyu Yang, Elle


Thank you to all my generous patrons and donaters!

<!-- footer end -->

# Original model card: OpenAssistant's Llama2 13B Orca v2 8K

- wandb: [jlhr5cf2](https://wandb.ai/open-assistant/supervised-finetuning/runs/jlhr5cf2)
- sampling-report: [2023-07-31_OpenAssistant_llama2-13b-orca-v2-8k-3166_sampling_llama2_prompt.json](https://open-assistant.github.io/oasst-model-eval/?f=https%3A%2F%2Fraw.githubusercontent.com%2FOpen-Assistant%2Foasst-model-eval%2Fmain%2Fsampling_reports%2Foasst-pretrained%2F2023-07-31_OpenAssistant_llama2-13b-orca-v2-8k-3166_sampling_llama2_prompt.json)


## Model Configuration
```
llama2-13b-orca-v2-8k:
  rng_seed: 0xe1291f21
  show_dataset_stats: true
  random_offset_probability: 0.0
  use_custom_sampler: true
  sort_by_length: false
  dtype: fp16
  log_dir: /mnt/data/ikka/data_cache/llama2_13b_orcav2_logs
  output_dir: /mnt/data/ikka/data_cache/llama2_13b_orcav2
  learning_rate: 1e-5
  model_name: conceptofmind/LLongMA-2-13b
  deepspeed_config: configs/zero_config_pretrain.json
  weight_decay: 0.000001
  max_length: 8192
  warmup_steps: 100
  peft_model: false
  use_flash_attention: true
  gradient_checkpointing: true
  gradient_accumulation_steps: 4
  per_device_train_batch_size: 2
  per_device_eval_batch_size: 1
  residual_dropout: 0.0
  eval_steps: 200
  save_steps: 200
  num_train_epochs: 1
  save_total_limit: 4
  superhot: false
  superhot_config:
    type: linear
    scaling_factor: 2
  datasets:
    - orca-chat: # shahules786/orca-chat
        data_files: orca-chat-gpt4-8k.json
        max_val_set: 5000
        val_split: 0.1
    - evol-codealpaca-v1: # theblackcat102/evol-codealpaca-v1
        fill_min_length: 20000
        val_split: 0.1
    - megacode: # rombodawg/MegaCodeTraining112k
        fill_min_length: 24000
        val_split: 0.1
        max_val_set: 1000
    - evol_instruct_code: # nickrosh/Evol-Instruct-Code-80k-v1
        fill_min_length: 24000
        val_split: 0.1
        max_val_set: 1000
```