Skywork-SWE-32B GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit 0142961a.

Quantization Beyond the IMatrix

I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.

In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the --tensor-type option in llama.cpp to manually "bump" important layers to higher precision. You can see the implementation here:
👉 Layer bumping with llama.cpp

While this does increase model file size, it significantly improves precision for a given quantization level.

I'd love your feedback—have you tried this? How does it perform for you?

Click here to get info on choosing the right GGUF model format

Skywork-SWE

📖 Technical Report | 📰 Blog

Model Introduction

Skywork-SWE-32B is a code agent model developed by Skywork AI, specifically designed for software engineering (SWE) tasks. It demonstrates strong performance across several key metrics:

Skywork-SWE-32B attains 38.0% pass@1 accuracy on the SWE-bench Verified benchmark, outperforming previous open-source SoTA Qwen2.5-Coder-32B-based LLMs built on the OpenHands agent framework.
When incorporated with test-time scaling techniques, the performance further improves to 47.0% 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 our technical report.

🔧 Model Details

Model Name	Backbone LLM	HuggingFace Link	Technical Report	Blog
Skywork-SWE-32B	🤗 Qwen2.5-Coder-32B-Instruct	🤗 Skywork-SWE-32B	Technical Report	Blog

Evaluation

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 significantly outperforms previous Qwen2.5-Coder-32B-based LLMs, achieving the highest pass@1 accuracy without using verifiers or multiple rollouts.

With the incorporation of test-time scaling techniques, Skywork-SWE-32B further improves to 47.0% 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 (Bo8):

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%)

Usage

Install vLLM package

# Install vLLM version 0.9.0.1.
# For example, if your CUDA version is 12.8, use the following command:
pip install vllm==0.9.0.1 --extra-index-url https://download.pytorch.org/whl/cu128

Launch a server to deploy Skywork-SWE-32B

vllm serve ${MODEL_PATH} —served-model-name ${SERVED_MODEL_NAME} --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.95 --tensor-parallel-size 8

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

The official documentation of OpenHands: SWE-Bench Evaluation with OpenHands SWE-Bench Docker Image

Create the corresponding config file:

[core]
workspace_base="./workspace"

[llm.my-oss-model]
model = "openai/${SERVED_MODEL_NAME}"
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

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 for detailed instructions. You will need to switch to the feature/llm-critic branch and deploy the critic model 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]

# 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 and AllHands Critic 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:

@misc{skywork-swe,
  title={Skywork-SWE: Unveiling Data Scaling Laws for Software Engineering in LLMs},
  author={Liang Zeng, Yongcong Li, Yuzhen Xiao, Changshi Li, Chris Yuhao Liu, Rui Yan, Tianwen Wei, Jujie He, Xuchen Song, Yang Liu, and Yahui Zhou},
  howpublished={\url{https://quixotic-sting-239.notion.site/eb17f379610040ceb54da5d5d24065bd}},
  note={Notion Blog},
    year={2025},
}

🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test:
Choose an AI assistant type:

TurboLLM (GPT-4.1-mini)
HugLLM (Hugginface Open-source models)
TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

Function calling against live network services
How small can a model go while still handling:
- Automated Nmap security scans
- Quantum-readiness checks
- Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

✅ Zero-configuration setup
⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

**It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
Create custom cmd processors to run .net code on Quantum Network Monitor Agents
Real-time network diagnostics and monitoring
Security Audits
Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test:

"Give me info on my websites SSL certificate"
"Check if my server is using quantum safe encyption for communication"
"Run a comprehensive security audit on my server"
'"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊

Mungert
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Skywork-SWE-32B-GGUF