Instructions to use AItonomy/PhAI-IDE-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AItonomy/PhAI-IDE-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="AItonomy/PhAI-IDE-9B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("AItonomy/PhAI-IDE-9B")
model = AutoModelForMultimodalLM.from_pretrained("AItonomy/PhAI-IDE-9B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AItonomy/PhAI-IDE-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AItonomy/PhAI-IDE-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AItonomy/PhAI-IDE-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/AItonomy/PhAI-IDE-9B

SGLang

How to use AItonomy/PhAI-IDE-9B 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 "AItonomy/PhAI-IDE-9B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AItonomy/PhAI-IDE-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "AItonomy/PhAI-IDE-9B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AItonomy/PhAI-IDE-9B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use AItonomy/PhAI-IDE-9B with Docker Model Runner:
```
docker model run hf.co/AItonomy/PhAI-IDE-9B
```

PhAI-IDE-9B / README.md

leyili6666

Simplify published comparisons to complete benchmark configurations

71fa225 verified about 2 months ago

preview code

Raw History Blame

8.56 kB

---
model_name: PhAI-IDE-9B
base_model: Qwen/Qwen3.5-9B
base_model_relation: finetune
datasets:
- AItonomy/ScienceIDE
library_name: transformers
pipeline_tag: image-text-to-text
license: apache-2.0
tags:
- phai-ide
- science
- code
- tool-use
- sft
- lora
- safetensors
---
# PhAI-IDE

**PhAI-IDE** is a family of models for scientific coding and interaction with tools, available in **4B, 9B, and 72B** sizes. Each model is supervised fine-tuned with [ms-swift](https://github.com/modelscope/ms-swift) and released as full BF16 weights with the final LoRA adapter merged, together with its configuration and tokenizer.

The **training dataset is Codex trajectories**, sourced from [AItonomy/ScienceIDE](https://huggingface.co/datasets/AItonomy/ScienceIDE).

## Models

| Model | Base model | BF16 weights | License |
| --- | --- | ---: | --- |
| [PhAI-IDE-4B](https://huggingface.co/AItonomy/PhAI-IDE-4B) | [Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) | 9.08 GB | Apache-2.0 |
| [PhAI-IDE-9B](https://huggingface.co/AItonomy/PhAI-IDE-9B) | [Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) | 18.82 GB | Apache-2.0 |
| [PhAI-IDE-72B](https://huggingface.co/AItonomy/PhAI-IDE-72B) | [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) | 145.41 GB | [Qwen](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/495f39366efef23836d0cfae4fbe635880d2be31/LICENSE) |

Weight sizes are approximate; inference also requires memory for runtime allocations and the KV cache.

## ScienceAccelBench performance

**PhAI-IDE-9B** improves the task-pooled pass rate from **17.09% to 20.89% (+3.80 percentage points)** across all **27 ScienceAccelBench environments**, using **158 valid paired tasks**.

Task-held-out, localized scientific-code repair on familiar codebases, with original numerical verification. Each row compares the initial model with its corresponding fine-tuned model on identical tasks. Pass rates are percentages; gains are percentage points.

| Model | Environment | Tasks | Initial model | PhAI-IDE | Gain (pp) |
| --- | --- | ---: | ---: | ---: | ---: |
| 4B | PLUTO-Particles-Dust | 3 | 0.00 | **33.33** | **+33.33** |
| 9B | LAPS | 16 | 31.25 | **50.00** | **+18.75** |
| 9B | MITgcm-biogeo | 8 | 0.00 | **12.50** | **+12.50** |
| 9B | PLUTO-RMHD | 7 | 0.00 | **28.57** | **+28.57** |

## Comparison with published models

Scores (%), grouped by benchmark and model size. Each reference entry gives its published score and the **PhAI-IDE score difference in percentage points**. Reference models are approximately the same size: 3–4B, 7–9B, and 67–72B, respectively.

| PhAI-IDE | Benchmark | Score | Reference models: score (difference) |
| --- | --- | ---: | --- |
| 4B | BBH multistep-arithmetic-two | **97.60** | [Llama-3.2-3B-Instruct](https://huggingface.co/spaces/steampunque/benchlm/blob/d45e8600172857935610426f797a4429f2f136d6/README.md) (3.21B): 53.2 (**+44.40**); [Phi-3.5-mini-8k-instruct](https://huggingface.co/spaces/steampunque/benchlm/blob/d45e8600172857935610426f797a4429f2f136d6/README.md) (3.82B): 95.6 (**+2.00**) |
| 9B | BBH word-sorting | **60.40** | [Llama-3.1-8B-Instruct](https://huggingface.co/spaces/steampunque/benchlm/blob/d45e8600172857935610426f797a4429f2f136d6/README.md) (8.03B): 51.2 (**+9.20**); [Qwen2.5-7B-Instruct](https://huggingface.co/spaces/steampunque/benchlm/blob/d45e8600172857935610426f797a4429f2f136d6/README.md) (7.62B): 15.6 (**+44.80**) |
| 9B | MATH-500 | **92.20** | [InternLM3-8B-Instruct](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm3-8b-instruct-gptq-int4) (8B): 83 (**+9.20**); [Qwen2.5-7B-Instruct](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm3-8b-instruct-gptq-int4) (7B): 72.4 (**+19.80**); [Llama-3.1-8B-Instruct](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm3-8b-instruct-gptq-int4) (8B): 48.4 (**+43.80**) |
| 72B | AQuA-RAT | **77.56** | [Llama-2-70B-Chat](https://openreview.net/pdf?id=FvfhHucpLd) (70B): 31.32 (**+46.24**) |
| 72B | ARC-Easy | **84.64** | [Llama-2-70B](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/evaluation/more_results.md) (70B): 76.5 (**+8.14**); [DeepSeek-LLM-67B-Chat](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/evaluation/more_results.md) (67B): 81.6 (**+3.04**) |
| 72B | ARC-Challenge | **64.42** | [Llama-2-70B](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/evaluation/more_results.md) (70B): 59.5 (**+4.92**); [DeepSeek-LLM-67B-Chat](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/evaluation/more_results.md) (67B): 64.1 (**+0.32**) |

Reference scores come from the linked publications, model cards, and independent evaluation reports; evaluation settings and sample counts vary by source. Differences describe reported scores across evaluations, rather than matched-protocol head-to-head gains. BBH entries refer to the named tasks.

## Quick start

Use Transformers 5.16.1, PyTorch and Accelerate. Set `model_id` to any model in the table above; the example selects the matching model class.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForImageTextToText
model_id = "AItonomy/PhAI-IDE-4B"
loader = AutoModelForCausalLM if model_id.endswith("72B") else AutoModelForImageTextToText
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = loader.from_pretrained(model_id, dtype="bfloat16", device_map="auto")
inputs = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Explain how to verify a numerical simulation."}],
    add_generation_prompt=True, enable_thinking=False, return_dict=True, return_tensors="pt",
).to(model.device)
output = model.generate(**inputs, max_new_tokens=128, do_sample=False)
print(tokenizer.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

## Training procedure

ScienceIDE demonstrations were collected with **GPT-5.6-sol** and filtered using a **numerical-equivalence verifier**. They capture code inspection, tool use, and responses to execution feedback.

All three models use **ms-swift** supervised fine-tuning with **LoRA across trainable linear layers for three epochs**. The release merges each final checkpoint's adapter into its base model. Retained assistant targets provide the next-token training signal, while conversation history and tool observations provide context. The trajectories retain the native `exec` / `wait` interaction format. Heuristic target masking selects assistant actions for supervision while preserving the surrounding interaction history.

| Shared setting | Value |
| --- | --- |
| Training dataset | Codex trajectories |
| Training examples / tasks | 4,567 segments / 564 tasks |
| Validation examples / tasks | 544 segments / 81 tasks |
| Train/validation task overlap | 0 |
| Training epochs | 3 |
| LoRA rank / alpha / dropout | 32 / 64 / 0.05 |
| Released weights | LoRA merged into BF16 Safetensors |

Long trajectories are organized into segments. Source partition assignments are preserved, with no task identifiers shared between training and validation.

For **4B and 9B**, fine-tuning uses text trajectories with the vision tower and aligner frozen. Their additional settings are:

| Setting | 4B and 9B |
| --- | --- |
| Maximum training sequence length | 36,864 tokens |
| Learning rate / schedule | 2e-5 / cosine |
| Warmup ratio | 0.03 |
| Batch size per device / accumulation | 1 / 16 |
| LoRA targets | Linear modules in the unfrozen language model |
| Optimizer steps | 216 |
| Packing | Disabled |

### Framework versions

The release was validated with the following environment.

| Component | Version |
| --- | --- |
| Python | 3.11 |
| ms-swift | 4.5.3 |
| Transformers | 5.16.1 |
| PyTorch | 2.6.0+cu124 |
| PEFT | 0.20.0 |
| Datasets | 4.8.4 |
| Tokenizers | 0.23.2 |
| Accelerate | 1.14.0 |

## Citations

The training framework is [ms-swift](https://github.com/modelscope/ms-swift), and adapter training uses [LoRA](https://arxiv.org/abs/2106.09685).

```bibtex
@misc{zhao2024swift,
  title = {SWIFT: A Scalable lightWeight Infrastructure for Fine-Tuning},
  author = {Yuze Zhao and others},
  year = {2024},
  eprint = {2408.05517},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2408.05517}
}
@misc{hu2021lora,
  title = {LoRA: Low-Rank Adaptation of Large Language Models},
  author = {Edward J. Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen},
  year = {2021},
  eprint = {2106.09685},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2106.09685}
}
```

	---
	model_name: PhAI-IDE-9B
	base_model: Qwen/Qwen3.5-9B
	base_model_relation: finetune
	datasets:
	- AItonomy/ScienceIDE
	library_name: transformers
	pipeline_tag: image-text-to-text
	license: apache-2.0
	tags:
	- phai-ide
	- science
	- code
	- tool-use
	- sft
	- lora
	- safetensors
	---

	# PhAI-IDE

	PhAI-IDE is a family of models for scientific coding and interaction with tools, available in 4B, 9B, and 72B sizes. Each model is supervised fine-tuned with [ms-swift](https://github.com/modelscope/ms-swift) and released as full BF16 weights with the final LoRA adapter merged, together with its configuration and tokenizer.

	The training dataset is Codex trajectories, sourced from [AItonomy/ScienceIDE](https://huggingface.co/datasets/AItonomy/ScienceIDE).

	## Models

	\| Model \| Base model \| BF16 weights \| License \|
	\| --- \| --- \| ---: \| --- \|
	\| [PhAI-IDE-4B](https://huggingface.co/AItonomy/PhAI-IDE-4B) \| [Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) \| 9.08 GB \| Apache-2.0 \|
	\| [PhAI-IDE-9B](https://huggingface.co/AItonomy/PhAI-IDE-9B) \| [Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) \| 18.82 GB \| Apache-2.0 \|
	\| [PhAI-IDE-72B](https://huggingface.co/AItonomy/PhAI-IDE-72B) \| [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) \| 145.41 GB \| [Qwen](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/495f39366efef23836d0cfae4fbe635880d2be31/LICENSE) \|

	Weight sizes are approximate; inference also requires memory for runtime allocations and the KV cache.

	## ScienceAccelBench performance

	PhAI-IDE-9B improves the task-pooled pass rate from 17.09% to 20.89% (+3.80 percentage points) across all 27 ScienceAccelBench environments, using 158 valid paired tasks.

	Task-held-out, localized scientific-code repair on familiar codebases, with original numerical verification. Each row compares the initial model with its corresponding fine-tuned model on identical tasks. Pass rates are percentages; gains are percentage points.

	\| Model \| Environment \| Tasks \| Initial model \| PhAI-IDE \| Gain (pp) \|
	\| --- \| --- \| ---: \| ---: \| ---: \| ---: \|
	\| 4B \| PLUTO-Particles-Dust \| 3 \| 0.00 \| 33.33 \| +33.33 \|
	\| 9B \| LAPS \| 16 \| 31.25 \| 50.00 \| +18.75 \|
	\| 9B \| MITgcm-biogeo \| 8 \| 0.00 \| 12.50 \| +12.50 \|
	\| 9B \| PLUTO-RMHD \| 7 \| 0.00 \| 28.57 \| +28.57 \|

	## Comparison with published models

	Scores (%), grouped by benchmark and model size. Each reference entry gives its published score and the PhAI-IDE score difference in percentage points. Reference models are approximately the same size: 3–4B, 7–9B, and 67–72B, respectively.

	\| PhAI-IDE \| Benchmark \| Score \| Reference models: score (difference) \|
	\| --- \| --- \| ---: \| --- \|
	\| 4B \| BBH multistep-arithmetic-two \| 97.60 \| [Llama-3.2-3B-Instruct](https://huggingface.co/spaces/steampunque/benchlm/blob/d45e8600172857935610426f797a4429f2f136d6/README.md) (3.21B): 53.2 (+44.40); [Phi-3.5-mini-8k-instruct](https://huggingface.co/spaces/steampunque/benchlm/blob/d45e8600172857935610426f797a4429f2f136d6/README.md) (3.82B): 95.6 (+2.00) \|
	\| 9B \| BBH word-sorting \| 60.40 \| [Llama-3.1-8B-Instruct](https://huggingface.co/spaces/steampunque/benchlm/blob/d45e8600172857935610426f797a4429f2f136d6/README.md) (8.03B): 51.2 (+9.20); [Qwen2.5-7B-Instruct](https://huggingface.co/spaces/steampunque/benchlm/blob/d45e8600172857935610426f797a4429f2f136d6/README.md) (7.62B): 15.6 (+44.80) \|
	\| 9B \| MATH-500 \| 92.20 \| [InternLM3-8B-Instruct](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm3-8b-instruct-gptq-int4) (8B): 83 (+9.20); [Qwen2.5-7B-Instruct](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm3-8b-instruct-gptq-int4) (7B): 72.4 (+19.80); [Llama-3.1-8B-Instruct](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm3-8b-instruct-gptq-int4) (8B): 48.4 (+43.80) \|
	\| 72B \| AQuA-RAT \| 77.56 \| [Llama-2-70B-Chat](https://openreview.net/pdf?id=FvfhHucpLd) (70B): 31.32 (+46.24) \|
	\| 72B \| ARC-Easy \| 84.64 \| [Llama-2-70B](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/evaluation/more_results.md) (70B): 76.5 (+8.14); [DeepSeek-LLM-67B-Chat](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/evaluation/more_results.md) (67B): 81.6 (+3.04) \|
	\| 72B \| ARC-Challenge \| 64.42 \| [Llama-2-70B](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/evaluation/more_results.md) (70B): 59.5 (+4.92); [DeepSeek-LLM-67B-Chat](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/evaluation/more_results.md) (67B): 64.1 (+0.32) \|

	Reference scores come from the linked publications, model cards, and independent evaluation reports; evaluation settings and sample counts vary by source. Differences describe reported scores across evaluations, rather than matched-protocol head-to-head gains. BBH entries refer to the named tasks.

	## Quick start

	Use Transformers 5.16.1, PyTorch and Accelerate. Set `model_id` to any model in the table above; the example selects the matching model class.

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForImageTextToText

	model_id = "AItonomy/PhAI-IDE-4B"
	loader = AutoModelForCausalLM if model_id.endswith("72B") else AutoModelForImageTextToText
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = loader.from_pretrained(model_id, dtype="bfloat16", device_map="auto")
	inputs = tokenizer.apply_chat_template(
	[{"role": "user", "content": "Explain how to verify a numerical simulation."}],
	add_generation_prompt=True, enable_thinking=False, return_dict=True, return_tensors="pt",
	).to(model.device)
	output = model.generate(**inputs, max_new_tokens=128, do_sample=False)
	print(tokenizer.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
	```

	## Training procedure

	ScienceIDE demonstrations were collected with GPT-5.6-sol and filtered using a numerical-equivalence verifier. They capture code inspection, tool use, and responses to execution feedback.

	All three models use ms-swift supervised fine-tuning with LoRA across trainable linear layers for three epochs. The release merges each final checkpoint's adapter into its base model. Retained assistant targets provide the next-token training signal, while conversation history and tool observations provide context. The trajectories retain the native `exec` / `wait` interaction format. Heuristic target masking selects assistant actions for supervision while preserving the surrounding interaction history.

	\| Shared setting \| Value \|
	\| --- \| --- \|
	\| Training dataset \| Codex trajectories \|
	\| Training examples / tasks \| 4,567 segments / 564 tasks \|
	\| Validation examples / tasks \| 544 segments / 81 tasks \|
	\| Train/validation task overlap \| 0 \|
	\| Training epochs \| 3 \|
	\| LoRA rank / alpha / dropout \| 32 / 64 / 0.05 \|
	\| Released weights \| LoRA merged into BF16 Safetensors \|

	Long trajectories are organized into segments. Source partition assignments are preserved, with no task identifiers shared between training and validation.

	For 4B and 9B, fine-tuning uses text trajectories with the vision tower and aligner frozen. Their additional settings are:

	\| Setting \| 4B and 9B \|
	\| --- \| --- \|
	\| Maximum training sequence length \| 36,864 tokens \|
	\| Learning rate / schedule \| 2e-5 / cosine \|
	\| Warmup ratio \| 0.03 \|
	\| Batch size per device / accumulation \| 1 / 16 \|
	\| LoRA targets \| Linear modules in the unfrozen language model \|
	\| Optimizer steps \| 216 \|
	\| Packing \| Disabled \|

	### Framework versions

	The release was validated with the following environment.

	\| Component \| Version \|
	\| --- \| --- \|
	\| Python \| 3.11 \|
	\| ms-swift \| 4.5.3 \|
	\| Transformers \| 5.16.1 \|
	\| PyTorch \| 2.6.0+cu124 \|
	\| PEFT \| 0.20.0 \|
	\| Datasets \| 4.8.4 \|
	\| Tokenizers \| 0.23.2 \|
	\| Accelerate \| 1.14.0 \|

	## Citations

	The training framework is [ms-swift](https://github.com/modelscope/ms-swift), and adapter training uses [LoRA](https://arxiv.org/abs/2106.09685).

	```bibtex
	@misc{zhao2024swift,
	title = {SWIFT: A Scalable lightWeight Infrastructure for Fine-Tuning},
	author = {Yuze Zhao and others},
	year = {2024},
	eprint = {2408.05517},
	archivePrefix = {arXiv},
	url = {https://arxiv.org/abs/2408.05517}
	}

	@misc{hu2021lora,
	title = {LoRA: Low-Rank Adaptation of Large Language Models},
	author = {Edward J. Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen},
	year = {2021},
	eprint = {2106.09685},
	archivePrefix = {arXiv},
	url = {https://arxiv.org/abs/2106.09685}
	}
	```