Instructions to use WizardLMTeam/WizardLM-13B-V1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WizardLMTeam/WizardLM-13B-V1.2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="WizardLMTeam/WizardLM-13B-V1.2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WizardLMTeam/WizardLM-13B-V1.2")
model = AutoModelForCausalLM.from_pretrained("WizardLMTeam/WizardLM-13B-V1.2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use WizardLMTeam/WizardLM-13B-V1.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WizardLMTeam/WizardLM-13B-V1.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WizardLMTeam/WizardLM-13B-V1.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WizardLMTeam/WizardLM-13B-V1.2

SGLang

How to use WizardLMTeam/WizardLM-13B-V1.2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "WizardLMTeam/WizardLM-13B-V1.2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WizardLMTeam/WizardLM-13B-V1.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "WizardLMTeam/WizardLM-13B-V1.2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WizardLMTeam/WizardLM-13B-V1.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WizardLMTeam/WizardLM-13B-V1.2 with Docker Model Runner:
```
docker model run hf.co/WizardLMTeam/WizardLM-13B-V1.2
```

WizardLM-13B-V1.2 / README.md

haipeng1

Update README.md

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---
license: llama2
---
This is the **Full-Weight** of WizardLM-13B V1.2 model, this model is trained from **Llama-2 13b**.

## 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://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>
## News

- 🔥🔥🔥[2023/08/26] We released **WizardCoder-Python-34B-V1.0** , which achieves the **73.2 pass@1** and surpasses **GPT4 (2023/03/15)**, **ChatGPT-3.5**, and **Claude2** on the [HumanEval Benchmarks](https://github.com/openai/human-eval). For more details, please refer to [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder).
- [2023/06/16] We released **WizardCoder-15B-V1.0** , which surpasses **Claude-Plus (+6.8)**, **Bard (+15.3)** and **InstructCodeT5+ (+22.3)** on the [HumanEval Benchmarks](https://github.com/openai/human-eval). For more details, please refer to [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder).

|  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-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 | [Demo](http://47.103.63.15:50086/) |  <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>  |


- 🔥 [08/11/2023] We release **WizardMath** Models.
- 🔥 Our **WizardMath-70B-V1.0** model slightly outperforms some closed-source LLMs on the GSM8K, including **ChatGPT 3.5**, **Claude Instant 1** and **PaLM 2 540B**.
- 🔥 Our **WizardMath-70B-V1.0** model achieves  **81.6 pass@1** on the [GSM8k Benchmarks](https://github.com/openai/grade-school-math), which is **24.8** points higher than the SOTA open-source LLM.
- 🔥 Our **WizardMath-70B-V1.0** model achieves  **22.7 pass@1** on the [MATH Benchmarks](https://github.com/hendrycks/math), which is **9.2** points higher than the SOTA open-source LLM.


| 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>WizardEval</sup> | <sup>HumanEval</sup>  | <sup>License</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>101.4% </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>99.3% </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>97.8% </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>89.1% </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>78.0% </sup> |<sup>19.1 pass@1 </sup>|<sup> Non-commercial</sup>|
</font>
**Repository**: https://github.com/nlpxucan/WizardLM

**Twitter**:


- 🔥🔥🔥 [7/25/2023] We released **WizardLM V1.2** models. The **WizardLM-13B-V1.2** is here ([Demo_13B-V1.2](https://b7a19878988c8c73.gradio.app), [Demo_13B-V1.2_bak-1](https://d0a37a76e0ac4b52.gradio.app/), [Full Model Weight](https://huggingface.co/WizardLM/WizardLM-13B-V1.2)). Please checkout the [paper](https://arxiv.org/abs/2304.12244).
- 🔥🔥🔥 [7/25/2023] The **WizardLM-13B-V1.2** achieves **7.06** on [MT-Bench Leaderboard](https://chat.lmsys.org/?leaderboard), **89.17%** on [AlpacaEval Leaderboard](https://tatsu-lab.github.io/alpaca_eval/), and **101.4%** on [WizardLM Eval](https://github.com/nlpxucan/WizardLM/blob/main/WizardLM/data/WizardLM_testset.jsonl). (Note: MT-Bench and AlpacaEval are all self-test, will push update and request review. All tests are completed under their official settings.)

❗<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: Hi ASSISTANT: Hello.</s>USER: Who are you? ASSISTANT: I am WizardLM.</s>......
```

❗<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.

	---
	license: llama2
	---
	This is the Full-Weight of WizardLM-13B V1.2 model, this model is trained from Llama-2 13b.

	## 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://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>

	## News

	- 🔥🔥🔥[2023/08/26] We released WizardCoder-Python-34B-V1.0 , which achieves the 73.2 pass@1 and surpasses GPT4 (2023/03/15), ChatGPT-3.5, and Claude2 on the [HumanEval Benchmarks](https://github.com/openai/human-eval). For more details, please refer to [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder).
	- [2023/06/16] We released WizardCoder-15B-V1.0 , which surpasses Claude-Plus (+6.8), Bard (+15.3) and InstructCodeT5+ (+22.3) on the [HumanEval Benchmarks](https://github.com/openai/human-eval). For more details, please refer to [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder).

	\| 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-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 \| [Demo](http://47.103.63.15:50086/) \| <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> \|


	- 🔥 [08/11/2023] We release WizardMath Models.
	- 🔥 Our WizardMath-70B-V1.0 model slightly outperforms some closed-source LLMs on the GSM8K, including ChatGPT 3.5, Claude Instant 1 and PaLM 2 540B.
	- 🔥 Our WizardMath-70B-V1.0 model achieves 81.6 pass@1 on the [GSM8k Benchmarks](https://github.com/openai/grade-school-math), which is 24.8 points higher than the SOTA open-source LLM.
	- 🔥 Our WizardMath-70B-V1.0 model achieves 22.7 pass@1 on the [MATH Benchmarks](https://github.com/hendrycks/math), which is 9.2 points higher than the SOTA open-source LLM.


	\| 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>WizardEval</sup> \| <sup>HumanEval</sup> \| <sup>License</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>101.4% </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>99.3% </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>97.8% </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>89.1% </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>78.0% </sup> \|<sup>19.1 pass@1 </sup>\|<sup> Non-commercial</sup>\|
	</font>

	Repository: https://github.com/nlpxucan/WizardLM

	Twitter:


	- 🔥🔥🔥 [7/25/2023] We released WizardLM V1.2 models. The WizardLM-13B-V1.2 is here ([Demo_13B-V1.2](https://b7a19878988c8c73.gradio.app), [Demo_13B-V1.2_bak-1](https://d0a37a76e0ac4b52.gradio.app/), [Full Model Weight](https://huggingface.co/WizardLM/WizardLM-13B-V1.2)). Please checkout the [paper](https://arxiv.org/abs/2304.12244).
	- 🔥🔥🔥 [7/25/2023] The WizardLM-13B-V1.2 achieves 7.06 on [MT-Bench Leaderboard](https://chat.lmsys.org/?leaderboard), 89.17% on [AlpacaEval Leaderboard](https://tatsu-lab.github.io/alpaca_eval/), and 101.4% on [WizardLM Eval](https://github.com/nlpxucan/WizardLM/blob/main/WizardLM/data/WizardLM_testset.jsonl). (Note: MT-Bench and AlpacaEval are all self-test, will push update and request review. All tests are completed under their official settings.)

	❗<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: Hi ASSISTANT: Hello.</s>USER: Who are you? ASSISTANT: I am WizardLM.</s>......
	```

	❗<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.