---
inference: false
license: other
model_creator: WizardLM
model_link: https://huggingface.co/WizardLM/WizardLM-70B-V1.0
model_name: WizardLM 70B V1.0
model_type: llama
quantized_by: TheBloke
---

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# WizardLM 70B V1.0 - GGML
- Model creator: [WizardLM](https://huggingface.co/WizardLM)
- Original model: [WizardLM 70B V1.0](https://huggingface.co/WizardLM/WizardLM-70B-V1.0)

## Description

This repo contains GGML format model files for [WizardLM's WizardLM 70B V1.0](https://huggingface.co/WizardLM/WizardLM-70B-V1.0).

GPU acceleration is now available for Llama 2 70B GGML files, with both CUDA (NVidia) and Metal (macOS). The following clients/libraries are known to work with these files, including with GPU acceleration:
* [llama.cpp](https://github.com/ggerganov/llama.cpp), commit `e76d630` and later.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), version 1.37 and later. A powerful GGML web UI, especially good for story telling.
* [LM Studio](https://lmstudio.ai/), a fully featured local GUI with GPU acceleration for both Windows and macOS. Use 0.1.11 or later for macOS GPU acceleration with 70B models.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), version 0.1.77 and later. A Python library with LangChain support, and OpenAI-compatible API server.
* [ctransformers](https://github.com/marella/ctransformers), version 0.2.15 and later. A Python library with LangChain support, and OpenAI-compatible API server.

## Repositories available

* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WizardLM-70B-V1.0-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardLM-70B-V1.0-GGML)
* [WizardLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/WizardLM/WizardLM-70B-V1.0)

## Prompt template: Vicuna

```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {prompt} ASSISTANT:
```

<!-- compatibility_ggml start -->
## Compatibility

### Requires llama.cpp [commit `e76d630`](https://github.com/ggerganov/llama.cpp/commit/e76d630df17e235e6b9ef416c45996765d2e36fb) or later.

Or one of the other tools and libraries listed above.

To use in llama.cpp, you must add `-gqa 8` argument.

For other UIs and libraries, please check the docs.

## 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [wizardlm-70b-v1.0.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/WizardLM-70B-V1.0-GGML/blob/main/wizardlm-70b-v1.0.ggmlv3.q2_K.bin) | q2_K | 2 | 28.96 GB| 31.46 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. |
| [wizardlm-70b-v1.0.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/WizardLM-70B-V1.0-GGML/blob/main/wizardlm-70b-v1.0.ggmlv3.q3_K_L.bin) | q3_K_L | 3 | 36.49 GB| 38.99 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 |
| [wizardlm-70b-v1.0.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/WizardLM-70B-V1.0-GGML/blob/main/wizardlm-70b-v1.0.ggmlv3.q3_K_M.bin) | q3_K_M | 3 | 33.39 GB| 35.89 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 |
| [wizardlm-70b-v1.0.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/WizardLM-70B-V1.0-GGML/blob/main/wizardlm-70b-v1.0.ggmlv3.q3_K_S.bin) | q3_K_S | 3 | 30.09 GB| 32.59 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| [wizardlm-70b-v1.0.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/WizardLM-70B-V1.0-GGML/blob/main/wizardlm-70b-v1.0.ggmlv3.q4_0.bin) | q4_0 | 4 | 38.80 GB| 41.30 GB | Original quant method, 4-bit. |
| [wizardlm-70b-v1.0.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/WizardLM-70B-V1.0-GGML/blob/main/wizardlm-70b-v1.0.ggmlv3.q4_1.bin) | q4_1 | 4 | 43.12 GB| 45.62 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. |
| [wizardlm-70b-v1.0.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/WizardLM-70B-V1.0-GGML/blob/main/wizardlm-70b-v1.0.ggmlv3.q4_K_M.bin) | q4_K_M | 4 | 41.69 GB| 44.19 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 |
| [wizardlm-70b-v1.0.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/WizardLM-70B-V1.0-GGML/blob/main/wizardlm-70b-v1.0.ggmlv3.q4_K_S.bin) | q4_K_S | 4 | 39.18 GB| 41.68 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| [wizardlm-70b-v1.0.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/WizardLM-70B-V1.0-GGML/blob/main/wizardlm-70b-v1.0.ggmlv3.q5_0.bin) | q5_0 | 5 | 47.43 GB| 49.93 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| [wizardlm-70b-v1.0.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/WizardLM-70B-V1.0-GGML/blob/main/wizardlm-70b-v1.0.ggmlv3.q5_K_M.bin) | q5_K_M | 5 | 49.03 GB| 51.53 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 |
| [wizardlm-70b-v1.0.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/WizardLM-70B-V1.0-GGML/blob/main/wizardlm-70b-v1.0.ggmlv3.q5_K_S.bin) | q5_K_S | 5 | 47.74 GB| 50.24 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| wizardlm-70b-v1.0.ggmlv3.q5_1.bin | q5_1 | 5 | 51.76 GB | 54.26 GB | Original quant method, 5-bit. Higher accuracy, slower inference than q5_0. |
| wizardlm-70b-v1.0.ggmlv3.q6_K.bin | q6_K | 6 | 56.59 GB | 59.09 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
| wizardlm-70b-v1.0.ggmlv3.q8_0.bin | q8_0 | 8 | 73.23 GB | 75.73 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.

### q5_1, 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.

<details>
  <summary>Click for instructions regarding q5_1, q6_K and q8_0 files</summary>
  
### q5_1
Please download:
* `wizardlm-70b-v1.0.ggmlv3.q5_1.zip`
* `wizardlm-70b-v1.0.ggmlv3.q5_1.z01`
 
### q6_K 
Please download:
* `wizardlm-70b-v1.0.ggmlv3.q6_K.zip`
* `wizardlm-70b-v1.0.ggmlv3.q6_K.z01`

### q8_0
Please download:
* `wizardlm-70b-v1.0.ggmlv3.q8_0.zip`
* `wizardlm-70b-v1.0.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 wizardlm-70b-v1.0.ggmlv3.q6_K.zip
```
</details>

## How to run in `llama.cpp`

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

```
./main -t 10 -ngl 40 -gqa 8 -m wizardlm-70b-v1.0.ggmlv3.q4_K_M.bin --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Write a story about llamas 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`. If you are fully offloading the model to GPU, use `-t 1`

Change `-ngl 40` to the number of GPU layers you have VRAM for. Use `-ngl 100` to offload all layers to VRAM - if you have a 48GB card, or 2 x 24GB, or similar.  Otherwise you can partially offload as many as you have VRAM for, on one or more GPUs.

If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`

Remember the `-gqa 8` argument, required for Llama 70B models.

Change `-c 4096` to the desired sequence length for this 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.

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-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).

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Thank you to all my generous patrons and donaters!

<!-- footer end -->

# Original model card: WizardLM's WizardLM 70B V1.0



## WizardLM: Empowering Large Pre-Trained Language Models to Follow Complex Instructions



<p align="center">
🤗 <a href="https://huggingface.co/WizardLM" target="_blank">HF Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a>  • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a>  <br>
</p>
<p align="center">
    👋 Join our <a href="https://discord.gg/bpmeZD7V" target="_blank">Discord</a>
</p>


<font size=4>
    
| <sup>Model</sup> | <sup>Checkpoint</sup> | <sup>Paper</sup> |<sup>MT-Bench</sup> | <sup>AlpacaEval</sup>  | <sup>GSM8k</sup> | <sup>HumanEval</sup>  | <sup>License</sup>|
| ----- |------| ---- |------|-------| ----- | ----- | ----- | 
| <sup>**WizardLM-70B-V1.0**</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-70B-V1.0" target="_blank">HF Link</a> </sup>|<sup>📃**Coming Soon**</sup>| <sup>**7.78**</sup> | <sup>**92.91%**</sup>	 |<sup>**77.6%**</sup>	 | <sup>   **50.6 pass@1**</sup>|<sup> <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a></sup> |
| <sup>WizardLM-13B-V1.2</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.2" target="_blank">HF Link</a> </sup>|  | <sup>7.06</sup> | <sup>89.17%</sup>	 |<sup>55.3%</sup>	 | <sup>36.6  pass@1</sup>|<sup> <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a></sup> |
| <sup>WizardLM-13B-V1.1</sup> |<sup> 🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.1" target="_blank">HF Link</a> </sup> |  | <sup>6.76</sup>  |<sup>86.32%</sup>	 | 	 | <sup>25.0  pass@1</sup>| <sup>Non-commercial</sup>|
| <sup>WizardLM-30B-V1.0</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-30B-V1.0" target="_blank">HF Link</a></sup>  | | <sup>7.01</sup> |                    | |  <sup>37.8  pass@1</sup>| <sup>Non-commercial</sup> |
| <sup>WizardLM-13B-V1.0</sup> | <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.0" target="_blank">HF Link</a> </sup> |  | <sup>6.35</sup> | <sup>75.31%</sup> |  | <sup> 24.0 pass@1 </sup> | <sup>Non-commercial</sup>|
| <sup>WizardLM-7B-V1.0 </sup>|  <sup>🤗 <a href="https://huggingface.co/WizardLM/WizardLM-7B-V1.0" target="_blank">HF Link</a> </sup> |<sup> 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> </sup>|  |  |  |<sup>19.1 pass@1 </sup>|<sup> Non-commercial</sup>|
| <sup>WizardCoder-15B-V1.0</sup> | <sup> 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-15B-V1.0" target="_blank">HF Link</a></sup>  | <sup>📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a></sup> |  | ||<sup> 57.3 pass@1 </sup> | <sup> <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a></sup> |
</font>

- 🔥🔥🔥 [08/09/2023] We released **WizardLM-70B-V1.0** model.

**Github Repo**: https://github.com/nlpxucan/WizardLM

**Twitter**: https://twitter.com/WizardLM_AI/status/1689270108747976704

**Discord**: https://discord.gg/bpmeZD7V



❗<b>Note for model system prompts usage:</b>


<b>WizardLM</b>  adopts the prompt format from <b>Vicuna</b> and supports **multi-turn** conversation. The prompt should be as following:

```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: hello, who are you? ASSISTANT: 
```