README.md

---
datasets:
- sciq
- metaeval/ScienceQA_text_only
- GAIR/lima
- Open-Orca/OpenOrca
- openbookqa
inference: false
language:
- en
license: other
model_type: llama
pipeline_tag: text-generation
tags:
- upstage
- llama
- instruct
- instruction
---

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# Upstage's Llama 65B Instruct GGML

These files are GGML format model files for [Upstage's Llama 65B Instruct](https://huggingface.co/upstage/llama-65b-instruct).

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/Upstage-Llama1-65B-Instruct-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Upstage-Llama1-65B-Instruct-GGML)
* [Original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/upstage/llama-65b-instruct)

## Prompt template: Orca-Hashes

```
### System:
{System}

### User:
{prompt}

### 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 They may be phased out soon, as they are largely superseded by the new k-quant methods.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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| upstage-llama-65b-instruct.ggmlv3.q2_K.bin | q2_K | 2 | 27.45 GB| 29.95 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. |
| upstage-llama-65b-instruct.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 34.65 GB| 37.15 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 |
| upstage-llama-65b-instruct.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 31.50 GB| 34.00 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 |
| upstage-llama-65b-instruct.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 28.16 GB| 30.66 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| upstage-llama-65b-instruct.ggmlv3.q4_0.bin | q4_0 | 4 | 36.73 GB| 39.23 GB | Original quant method, 4-bit. |
| upstage-llama-65b-instruct.ggmlv3.q4_1.bin | q4_1 | 4 | 40.81 GB| 43.31 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. |
| upstage-llama-65b-instruct.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 39.35 GB| 41.85 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 |
| upstage-llama-65b-instruct.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 36.80 GB| 39.30 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| upstage-llama-65b-instruct.ggmlv3.q5_0.bin | q5_0 | 5 | 44.89 GB| 47.39 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| upstage-llama-65b-instruct.ggmlv3.q5_1.bin | q5_1 | 5 | 48.97 GB| 51.47 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| upstage-llama-65b-instruct.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 46.24 GB| 48.74 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 |
| upstage-llama-65b-instruct.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 44.92 GB| 47.42 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| upstage-llama-65b-instruct.ggmlv3.q6_K.bin | q6_K | 6 | 53.56 GB | 56.06 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
| upstage-llama-65b-instruct.ggmlv3.q8_0.bin | q8_0 | 8 | 69.370 GB | 71.87 GB | Original llama.cpp 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.

### q6_K and q8_0 files require expansion from archive

**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the q6_K and q8_0 files as multi-part ZIP files. They are not compressed, they are just for storing a .bin file in two parts.

### q6_K 
Please download:
* `upstage-llama-65b-instruct.ggmlv3.q6_K.zip`
* `upstage-llama-65b-instruct.ggmlv3.q6_K.z01`

### q8_0
Please download:
* `upstage-llama-65b-instruct.ggmlv3.q8_0.zip`
* `upstage-llama-65b-instruct.ggmlv3.q8_0.z01`

Then extract the .zip archive. This will will expand both parts automatically. On Linux I found I had to use `7zip` - the basic `unzip` tool did not work. Example:
```
sudo apt update -y && sudo apt install 7zip
7zz x upstage-llama-65b-instruct.ggmlv3.q6_K.zip
```

Once the `.bin` is extracted you can delete the `.zip` and `.z01` files.

## How to run in `llama.cpp`

I use the following command line; adjust for your tastes and needs:

```
./main -t 10 -ngl 32 -m upstage-llama-65b-instruct.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### System: You are a helpful assistant\n### User: write a story about llamas\n### Assistant:"
```
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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# Original model card: Upstage's Llama 65B Instruct

# LLaMa-65b-instruct model card

## Contact Us, Why Upstage LLM?
- [Upstage](https://en.upstage.ai)'s LLM research has yielded remarkable results. Our 30B model **outperforms all models around the world**,  positioning itself as the leading performer. Recognizing the immense potential in implementing private LLM to actual businesses, we invite you to easily apply private LLM and fine-tune it with your own data. For a seamless and tailored solution, please do not hesitate to reach out to us. ► [click here to contact].

## Model and Dataset Details
- Please refer to the model card of [upstage/llama-30b-instruct](https://huggingface.co/upstage/llama-30b-instruct) as this one is almost the same.

## License
- This model is under a **Non-commercial** Bespoke License and governed by the Meta license. You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform), but have either lost your copy of the weights or encountered issues converting them to the Transformers format.

[click here to contact]: mailto:[email protected]