initial readme

README.md

@@ -1,138 +1,158 @@
----
-license: apache-2.0
-license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct/blob/main/LICENSE
-language:
-- en
-base_model:
-- Qwen/Qwen2.5-Coder-32B
-pipeline_tag: text-generation
-library_name: transformers
-tags:
-- code
-- codeqwen
-- chat
-- qwen
-- qwen-coder
----
-
-
-# Qwen2.5-Coder-32B-Instruct
-<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
-    <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
-</a>
-
-## Introduction
-
-Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:
-
-- Significantly improvements in **code generation**, **code reasoning** and **code fixing**. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o.
-- A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies.
-- **Long-context Support** up to 128K tokens.
-
-**This repo contains the instruction-tuned 32B Qwen2.5-Coder model**, which has the following features:
-- Type: Causal Language Models
-- Training Stage: Pretraining & Post-training
-- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
-- Number of Parameters: 32.5B
-- Number of Paramaters (Non-Embedding): 31.0B
-- Number of Layers: 64
-- Number of Attention Heads (GQA): 40 for Q and 8 for KV
-- Context Length: Full 131,072 tokens
-  - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
-  
-For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/), [GitHub](https://github.com/QwenLM/Qwen2.5-Coder), [Documentation](https://qwen.readthedocs.io/en/latest/), [Arxiv](https://arxiv.org/abs/2409.12186).
-
-## Requirements
-
-The code of Qwen2.5-Coder has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
-
-With `transformers<4.37.0`, you will encounter the following error:
-```
-KeyError: 'qwen2'
-```
-
-## Quickstart
-
-Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "Qwen/Qwen2.5-Coder-32B-Instruct"
-
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    torch_dtype="auto",
-    device_map="auto"
-)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-prompt = "write a quick sort algorithm."
-messages = [
-    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
-    {"role": "user", "content": prompt}
-]
-text = tokenizer.apply_chat_template(
-    messages,
-    tokenize=False,
-    add_generation_prompt=True
-)
-model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
-
-generated_ids = model.generate(
-    **model_inputs,
-    max_new_tokens=512
-)
-generated_ids = [
-    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
-]
-
-response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-```
-
-### Processing Long Texts
-
-The current `config.json` is set for context length up to 32,768 tokens.
-To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
-
-For supported frameworks, you could add the following to `config.json` to enable YaRN:
-```json
-{
-  ...,
-  "rope_scaling": {
-    "factor": 4.0,
-    "original_max_position_embeddings": 32768,
-    "type": "yarn"
-  }
-}
+# Skywork-SWE
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6665dd2b3a64c70529f7542c/KJezzkcswgRQDkC9MvKXg.png)
+
+📖 [Report]()  📰 [Blog](https://quixotic-sting-239.notion.site/eb17f379610040ceb54da5d5d24065bd)
+
+## Model Introduction
+***Skywork-SWE-32B*** is a code agent model specifically designed for software engineering (SWE) tasks developed by [Skywork.AI](https://skywork.ai/home). It achieves state-of-the-art performance across several key metrics:
+- Skywork-SWE-32B attains 38.0% pass@1 accuracy on the [SWE-bench Verified](https://huggingface.co/datasets/princeton-nlp/SWE-bench_Verified) benchmark, outperforming previous open-source SOTA [Qwen2.5-Coder-32B-based](https://huggingface.co/Qwen/Qwen2.5-Coder-32B) LLMs built on the [OpenHands](https://www.all-hands.dev/) agent framework. 
+- When incorporated with test-time scaling techniques, the performance further improves to 47.0% pass@1 accuracy, surpassing the previous SoTA results for sub-32B parameter models.
+- We clearly demonstrate the data scaling law phenomenon for software engineering capabilities in LLMs, with no signs of saturation at 8209 collected training trajectories.
+
+We also introduce an efficient and automated pipeline for SWE data collection, culminating in the creation of the Skywork-SWE dataset---a large-scale, high-quality dataset featuring comprehensive executable runtime environments. Detailed descriptions are available on [arXiv](https://xxx).
+### 🔧 Model Details
+
+| Model Name | Backbone LLM | HuggingFace Link | Technology Report | Blog |
+|---|---------------|-----------|-|-|
+|Skywork-SWE-32B | [🤗 Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) | [🤗 Skywork-SWE-32B](https://huggingface.co/Skywork/Skywork-SWE-32B) | [arXiv]() | [blog]()|
+
+## Evaluation
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6665dd2b3a64c70529f7542c/_ywqwtZYRcNO4jvYLb6dp.png)
+
+Data Scaling Law for Pass@1 Accuracy on Qwen2.5-Coder-32B-Based LLMs Using the OpenHands v0.32.0 Code Agent Framework. Skywork-SWE-32B establishes a new state-of-the-art (SoTA) among the Qwen2.5-Coder-32B-based LLM, achieving the highest pass@1 accuracy without using verifiers or multiple rollouts.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6665dd2b3a64c70529f7542c/K70TVU8eeRrFPf3tArIao.png)
+
+With the incorporation of test-time scaling techniques, Skywork-SWE-32B further improves to 47.0% pass@1 accuracy, surpassing the previous SoTA results for sub-32B parameter models.
+
+## Performance Summary
+- Skywork-SWE-32B:
+```
+Submission summary on SWE-bench verified split
+==================================================
+Resolved 190 instances (38.0%)
+==================================================
+Resolved by Repository
+- astropy/astropy: 4/22 (18.18%)
+- django/django: 99/231 (42.86%)
+- matplotlib/matplotlib: 9/34 (26.47%)
+- mwaskom/seaborn: 0/2 (0.0%)
+- pallets/flask: 1/1 (100.0%)
+- psf/requests: 4/8 (50.0%)
+- pydata/xarray: 7/22 (31.82%)
+- pylint-dev/pylint: 2/10 (20.0%)
+- pytest-dev/pytest: 9/19 (47.37%)
+- scikit-learn/scikit-learn: 17/32 (53.12%)
+- sphinx-doc/sphinx: 13/44 (29.55%)
+- sympy/sympy: 25/75 (33.33%)
+==================================================
+Resolved by Time
+- 2013: 2/3 (66.67%)
+- 2014: 2/2 (100.0%)
+- 2015: 0/1 (0.0%)
+- 2016: 2/2 (100.0%)
+- 2017: 5/16 (31.25%)
+- 2018: 7/24 (29.17%)
+- 2019: 46/98 (46.94%)
+- 2020: 43/108 (39.81%)
+- 2021: 27/86 (31.4%)
+- 2022: 35/102 (34.31%)
+- 2023: 21/58 (36.21%)
+```
+- Skywork-SWE-32B + TTS:
+```
+Submission summary on SWE-bench verified split
+==================================================
+Resolved 235 instances (47.0%)
+==================================================
+Resolved by Repository
+- astropy/astropy: 8/22 (36.36%)
+- django/django: 115/231 (49.78%)
+- matplotlib/matplotlib: 15/34 (44.12%)
+- mwaskom/seaborn: 0/2 (0.0%)
+- pallets/flask: 1/1 (100.0%)
+- psf/requests: 3/8 (37.5%)
+- pydata/xarray: 14/22 (63.64%)
+- pylint-dev/pylint: 4/10 (40.0%)
+- pytest-dev/pytest: 10/19 (52.63%)
+- scikit-learn/scikit-learn: 22/32 (68.75%)
+- sphinx-doc/sphinx: 12/44 (27.27%)
+- sympy/sympy: 31/75 (41.33%)
+==================================================
+Resolved by Time
+- 2013: 1/3 (33.33%)
+- 2014: 1/2 (50.0%)
+- 2015: 0/1 (0.0%)
+- 2016: 2/2 (100.0%)
+- 2017: 6/16 (37.5%)
+- 2018: 9/24 (37.5%)
+- 2019: 52/98 (53.06%)
+- 2020: 48/108 (44.44%)
+- 2021: 40/86 (46.51%)
+- 2022: 46/102 (45.1%)
+- 2023: 30/58 (51.72%)
 ```
 
-For deployment, we recommend using vLLM. 
-Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM.
-Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**. 
-We advise adding the `rope_scaling` configuration only when processing long contexts is required.
+## Usage
 
-## Evaluation & Performance
+### Launch a server to deploy Skywork-SWE-32B
 
-Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/).
+You can serve the model using vLLM or SGLang. Since our model has 32 billion parameters and supports a 32K context length, we recommend launching the model server with at least 2 GPUs equipped with sufficient VRAM to ensure efficient inference.
+### Set up OpenHands framework
+```
+git clone https://github.com/All-Hands-AI/OpenHands.git
+cd OpenHands
+git checkout tags/0.32.0
+make build
+```
+See official documentation for more details: [SWE-Bench Evaluation with OpenHands SWE-Bench Docker Image](https://github.com/All-Hands-AI/OpenHands/tree/main/evaluation/benchmarks/swe_bench)
 
-For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
+###  Create the corresponding config file:
+```
+[core]
+workspace_base="./workspace"
+
+[llm.my-oss-model]
+model = "openai//path/to/Skywork-SWE "
+base_url = "http://0.0.0.0:8000/v1"
+api_key="vllm"
+max_message_chars=32768
+max_input_tokens=32768
+max_output_tokens=8192
+log_completions=true
+temperature=0.0
+```
 
-## Citation
+If you want to run the OpenHands agent with test-time scaling techniques (a Best-of-N method based on the critic model), please refer to the [blog](https://www.all-hands.dev/blog/sota-on-swe-bench-verified-with-inference-time-scaling-and-critic-model) for detailed instructions. You will need to switch to the [feature/llm-critic](https://github.com/All-Hands-AI/OpenHands/tree/feature/llm-critic) branch and deploy the [critic model](https://huggingface.co/all-hands/openhands-critic-32b-exp-20250417) accordingly. Additionally, you need to add the following parameters into the configuration file:
+```
+use_critic=true
+critic_model="critic_model"
+critic_base_url="**********"
+critic_api_key="************"
+critic_num_candidates=2
+```
+
+### Rollout on SWE-Bench Instances
+```
+./evaluation/benchmarks/swe_bench/scripts/run_infer.sh [model_config] [git-version] [agent] [eval_limit] [max_iter] [num_workers] [dataset] [dataset_split]
 
-If you find our work helpful, feel free to give us a cite.
-
-```
-@article{hui2024qwen2,
-      title={Qwen2. 5-Coder Technical Report},
-      author={Hui, Binyuan and Yang, Jian and Cui, Zeyu and Yang, Jiaxi and Liu, Dayiheng and Zhang, Lei and Liu, Tianyu and Zhang, Jiajun and Yu, Bowen and Dang, Kai and others},
-      journal={arXiv preprint arXiv:2409.12186},
-      year={2024}
-}
-@article{qwen2,
-      title={Qwen2 Technical Report}, 
-      author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
-      journal={arXiv preprint arXiv:2407.10671},
-      year={2024}
-}
+# Example
+./evaluation/benchmarks/swe_bench/scripts/run_infer.sh llm.my-oss-model HEAD CodeActAgent 500 100 1 
+princeton-nlp/SWE-bench_Verified test
+```
+
+### Evaluate generated patches
+```
+./evaluation/benchmarks/swe_bench/scripts/eval_infer.sh \
+./evaluation_outputs/outputs/princeton-nlp__SWE-bench_Lite-test/CodeActAgent/my-oss-model_maxiter_100_N_v0.32.0-no-hint-run_1/output.jsonl
+```
+
+## Acknowledgements
+We would like to thank the contributors of the [OpenHands](https://www.all-hands.dev/) and [AllHands Critic](https://huggingface.co/all-hands/openhands-critic-32b-exp-20250417) repositories for their open research and valuable contributions.
+
+## Citation
+If you use Skywork-SWE in your research, please consider citing our work using the following BibTeX entry:
+```
 ```