auto-round

v0.13.0 suspicious
5.0
Medium Risk

Repository of AutoRound: Advanced Weight-Only Quantization Algorithm for LLMs

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential obfuscation and has a new maintainer with limited history, raising concerns about its integrity.

  • Unusual code formatting and use of eval() indicating possible obfuscation.
  • New maintainer with limited historical contributions.
Per-check LLM notes
  • Network: No network calls detected.
  • Shell: Shell execution appears to be for version control purposes and not indicative of malicious activity.
  • Obfuscation: The code uses unusual formatting and eval(), which is often used for obfuscation to hide malicious intent.
  • Credentials: No clear patterns indicating credential harvesting were found.
  • Metadata: The package has a non-secure external link and a new maintainer with limited history, raising some concerns.

📦 Package Quality Overall: Low (4.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (21418 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 256 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 18 unique contributor(s) across 100 commits in intel/auto-round
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • , ) else: eval(args) def run(): if "list" in sys.argv or "--list" in
  • ) -> None: self.model.eval() # Keep rotation matrices on the model — they are
  • .deepcopy(model) original.eval() for p in original.parameters(): p.requires_gra
  • thogonality(model) model.eval() return TrainingResult( loss_history=loss_hist
  • return {} self.model.eval() device = next(self.model.parameters()).device
  • ty_cache() self.model.eval() def _trigger_event(self, event_name: str, **kwargs) -
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • (): try: result = subprocess.run( ["git", "describe", "--exact-match", "--tags"],
  • n__ try: result = subprocess.run(["git", "describe", "--tags"], capture_output=True, text=Tru
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: intel.com

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://arxiv.org/abs/2512.04746
✓ Git Repository History

Repository intel/auto-round appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Intel AIPT Team" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with auto-round 
Develop a mini-application named 'QuantumText' which leverages the 'auto-round' Python package to optimize and quantize weights of a pre-trained language model for deployment on resource-constrained devices. QuantumText aims to demonstrate the practical benefits of weight-only quantization techniques in reducing memory usage and computational requirements while maintaining high performance accuracy.

Step-by-step guide:
1. Choose a popular pre-trained language model (e.g., GPT-2 or BERT) as the base model for your application.
2. Integrate the 'auto-round' package into your project to enable advanced weight-only quantization capabilities.
3. Implement a user-friendly interface where users can input text prompts and receive optimized responses from the quantized model.
4. Develop a feature to compare the performance metrics (such as latency, memory usage, and response quality) between the original model and the quantized version.
5. Ensure that the application logs and displays the quantization process details, including the level of precision achieved and any trade-offs made.
6. Include an option for users to fine-tune the quantization parameters if desired, allowing for customization based on specific use cases or constraints.
7. Provide documentation and examples to help other developers understand how to integrate and utilize the 'auto-round' package within their own projects.

Suggested Features:
- Real-time demonstration of quantization effects on model outputs.
- Visualizations of memory usage reduction and speed improvements post-quantization.
- Support for multiple quantization levels (e.g., 8-bit, 16-bit).
- Compatibility with various deep learning frameworks (PyTorch, TensorFlow).
- Detailed logging and reporting tools for analyzing the impact of different quantization strategies.

💬 Discussion Feed

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