---
license: llama2
model_name: WizardMath 7B V1.0
inference: false
model_creator: WizardLM
model_link: https://huggingface.co/WizardLM/WizardMath-7b-V1.0
model_type: llama
quantized_by: TheBloke
base_model: WizardLM/WizardMath-7b-V1.0
---

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# WizardMath 7B V1.0 - GGML
- Model creator: [WizardLM](https://huggingface.co/WizardLM)
- Original model: [WizardMath 7B V1.0](https://huggingface.co/WizardLM/WizardMath-7b-V1.0)

## Description

This repo contains GGML format model files for [WizardLM's WizardMath 7B V1.0](https://huggingface.co/WizardLM/WizardMath-7b-V1.0).

### 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The GGML format has now been superseded by GGUF.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/WizardMath-7B-V1.0-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardMath-7B-V1.0-GGUF)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/WizardMath-7B-V1.0-GGML)
* [WizardLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/WizardLM/WizardMath-7b-V1.0)

## Prompt template: Alpaca-CoT

```
Below is an instruction that describes a task. Write a response that appropriately completes the request.


### Instruction:
{prompt}


### Response: Let's think step by step.

```

<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [wizardmath-7b-v1.0.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/WizardMath-7B-V1.0-GGML/blob/main/wizardmath-7b-v1.0.ggmlv3.q2_K.bin) | q2_K | 2 | 3.05 GB| 5.55 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. |
| [wizardmath-7b-v1.0.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/WizardMath-7B-V1.0-GGML/blob/main/wizardmath-7b-v1.0.ggmlv3.q3_K_S.bin) | q3_K_S | 3 | 3.12 GB| 5.62 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| [wizardmath-7b-v1.0.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/WizardMath-7B-V1.0-GGML/blob/main/wizardmath-7b-v1.0.ggmlv3.q3_K_M.bin) | q3_K_M | 3 | 3.45 GB| 5.95 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 |
| [wizardmath-7b-v1.0.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/WizardMath-7B-V1.0-GGML/blob/main/wizardmath-7b-v1.0.ggmlv3.q3_K_L.bin) | q3_K_L | 3 | 3.77 GB| 6.27 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 |
| [wizardmath-7b-v1.0.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/WizardMath-7B-V1.0-GGML/blob/main/wizardmath-7b-v1.0.ggmlv3.q4_0.bin) | q4_0 | 4 | 3.79 GB| 6.29 GB | Original quant method, 4-bit. |
| [wizardmath-7b-v1.0.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/WizardMath-7B-V1.0-GGML/blob/main/wizardmath-7b-v1.0.ggmlv3.q4_K_S.bin) | q4_K_S | 4 | 3.98 GB| 6.48 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| [wizardmath-7b-v1.0.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/WizardMath-7B-V1.0-GGML/blob/main/wizardmath-7b-v1.0.ggmlv3.q4_1.bin) | q4_1 | 4 | 4.21 GB| 6.71 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. |
| [wizardmath-7b-v1.0.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/WizardMath-7B-V1.0-GGML/blob/main/wizardmath-7b-v1.0.ggmlv3.q4_K_M.bin) | q4_K_M | 4 | 4.24 GB| 6.74 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| [wizardmath-7b-v1.0.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/WizardMath-7B-V1.0-GGML/blob/main/wizardmath-7b-v1.0.ggmlv3.q5_0.bin) | q5_0 | 5 | 4.63 GB| 7.13 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| [wizardmath-7b-v1.0.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/WizardMath-7B-V1.0-GGML/blob/main/wizardmath-7b-v1.0.ggmlv3.q5_K_S.bin) | q5_K_S | 5 | 4.79 GB| 7.29 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| [wizardmath-7b-v1.0.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/WizardMath-7B-V1.0-GGML/blob/main/wizardmath-7b-v1.0.ggmlv3.q5_K_M.bin) | q5_K_M | 5 | 4.92 GB| 7.42 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 |
| [wizardmath-7b-v1.0.ggmlv3.q5_1.bin](https://huggingface.co/TheBloke/WizardMath-7B-V1.0-GGML/blob/main/wizardmath-7b-v1.0.ggmlv3.q5_1.bin) | q5_1 | 5 | 5.06 GB| 7.56 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| [wizardmath-7b-v1.0.ggmlv3.q6_K.bin](https://huggingface.co/TheBloke/WizardMath-7B-V1.0-GGML/blob/main/wizardmath-7b-v1.0.ggmlv3.q6_K.bin) | q6_K | 6 | 5.65 GB| 8.15 GB | New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization |
| [wizardmath-7b-v1.0.ggmlv3.q8_0.bin](https://huggingface.co/TheBloke/WizardMath-7B-V1.0-GGML/blob/main/wizardmath-7b-v1.0.ggmlv3.q8_0.bin) | q8_0 | 8 | 7.16 GB| 9.66 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 wizardmath-7b-v1.0.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\n### Instruction:\nWrite a story about llamas\n\n\n### Response: Let's think step by step."
```
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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## Thanks, and how to contribute.

Thanks to the [chirper.ai](https://chirper.ai) team!

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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.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

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Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

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# Original model card: WizardLM's WizardMath 7B V1.0



## WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct (RLEIF)


<p align="center">
🤗 <a href="https://huggingface.co/WizardLM" target="_blank">HF Repo</a>  •🐱 <a href="https://github.com/nlpxucan/WizardLM" target="_blank">Github 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>   • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>  <br>
</p>
<p align="center">
    👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a>
</p>

|  Model  |  Checkpoint  | Paper    | HumanEval  |   MBPP | Demo | License |
| ----- |------| ---- |------|-------| ----- |  ----- | 
|  WizardCoder-Python-34B-V1.0  |   🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0" target="_blank">HF Link</a>   |  📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a>  |  73.2   | 61.2 | [Demo](http://47.103.63.15:50085/) |  <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a>  |
|  WizardCoder-15B-V1.0  |   🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-15B-V1.0" target="_blank">HF Link</a>   |  📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a>  |  59.8   |50.6 | -- |  <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a>  |
|  WizardCoder-Python-13B-V1.0  |   🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-13B-V1.0" target="_blank">HF Link</a>   |  📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a>  |  64.0   | 55.6 | -- |  <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a>  |
|  WizardCoder-Python-7B-V1.0  |   🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-7B-V1.0" target="_blank">HF Link</a>   |  📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a>  |  55.5   | 51.6 | [Demo](http://47.103.63.15:50088/) |  <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a>  |
|  WizardCoder-3B-V1.0  |   🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-3B-V1.0" target="_blank">HF Link</a>   |  📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a>  |  34.8   |37.4 | -- |  <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a>  |
|  WizardCoder-1B-V1.0  |   🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-1B-V1.0" target="_blank">HF Link</a>   |  📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a>  |  23.8   |28.6 | -- |  <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a>  |


| Model | Checkpoint | Paper  | GSM8k | MATH  |Online Demo| License|
| ----- |------| ---- |------|-------| ----- | ----- |
| WizardMath-70B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-70B-V1.0" target="_blank">HF Link</a> |  📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **81.6**  |  **22.7**	|[Demo](http://47.103.63.15:50083/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2  </a> |
| WizardMath-13B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-13B-V1.0" target="_blank">HF Link</a> |  📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **63.9**  |  **14.0** |[Demo](http://47.103.63.15:50082/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> |
| WizardMath-7B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.0" target="_blank">HF Link</a>  |  📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| 	 **54.9**  |  **10.7** | [Demo](http://47.103.63.15:50080/)|  <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2  </a>|    

   

<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>|
</font>

**Github Repo**: https://github.com/nlpxucan/WizardLM/tree/main/WizardMath

**Twitter**: https://twitter.com/WizardLM_AI/status/1689998428200112128

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

## Comparing WizardMath-V1.0 with Other LLMs.

🔥 The following figure shows that our **WizardMath-70B-V1.0 attains the fifth position in this benchmark**, surpassing ChatGPT (81.6 vs. 80.8) , Claude Instant (81.6 vs. 80.9), PaLM 2 540B (81.6 vs. 80.7).

<p align="center" width="100%">
<a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/WizardMath/images/wizardmath_gsm8k.png" alt="WizardMath" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
</p>

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

Please use **the same systems prompts strictly** with us, and we do not guarantee the accuracy of the **quantified versions**.

**Default version:**

```
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
```


**CoT Version:** （❗For the **simple** math questions, we do NOT recommend to use the CoT prompt.） 


```
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response: Let's think step by step."
```

## Inference WizardMath Demo Script

We provide the WizardMath inference demo code [here](https://github.com/nlpxucan/WizardLM/tree/main/demo).


❗<b>To commen concern about dataset:</b>

Recently, there have been clear changes in the open-source policy and regulations of our overall organization's code, data, and models. 
Despite this, we have still worked hard to obtain opening the weights of the model first, but the data involves stricter auditing and is in review with our legal team .
Our researchers have no authority to publicly release them without authorization.
Thank you for your understanding.


## Citation

Please cite the repo if you use the data, method or code in this repo.

```
@article{luo2023wizardmath,
  title={WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct},
  author={Luo, Haipeng and Sun, Qingfeng and Xu, Can and Zhao, Pu and Lou, Jianguang and Tao, Chongyang and Geng, Xiubo and Lin, Qingwei and Chen, Shifeng and Zhang, Dongmei},
  journal={arXiv preprint arXiv:2308.09583},
  year={2023}
}
```