amazingvmsloth

v0.6.7 suspicious
5.0
Medium Risk

Blazing-fast LLM fine-tuning with minimal VRAM — multi-GPU, manual LoRA gradients, flash attention, 4-bit quant, web dashboard

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks due to its use of subprocess execution and base64 obfuscation, indicating potential for hidden functionality or malicious intent.

  • High shell risk due to subprocess usage
  • Moderate obfuscation risk from base64 encoding
Per-check LLM notes
  • Network: The use of urllib for network calls is common but could be used for unexpected data transfers.
  • Shell: Subprocess execution can be legitimate but also indicates potential for executing arbitrary commands, which raises concern.
  • Obfuscation: The use of base64 decoding suggests potential obfuscation to hide code logic or data, which could be suspicious without clear documentation.
  • Credentials: No clear patterns indicative of credential harvesting were found.
  • Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising some suspicion but not definitive evidence of malice.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

  • Test runner config found: pyproject.toml
  • 3 test file(s) detected (e.g. test_lora.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (5195 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

  • 135 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • with urllib.request.urlopen(path, timeout=30) as resp:
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • path_or_bytes = base64.b64decode(b64) img = Image.open(path_or_bytes) eli
  • path_or_bytes = base64.b64decode(b64) # Try librosa first (best quality)
  • meters()).dtype model.eval() t0 = time.time() with torch.no_grad():
  • odel.to(device) model.eval() print(f"[infer] VITS loaded: {sum(p.numel() for p
  • odel.to(device) model.eval() print(f"[infer] Loaded toy {model_type} with {sum(
  • Layer(base, config) layer.eval() x = torch.randn(2, 10, 128) base_out = base(x)
⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • try: result = subprocess.run([sys.executable, "-m", "gguf.scripts.convert_hf_to_gguf", "-
  • n") try: result = subprocess.run(cmd, check=True) print(f"\n SUCCESS: GGUF saved to
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with amazingvmsloth 
Create a personalized poetry generator mini-application using the 'amazingvmsloth' Python package. This application will leverage the package's capabilities for efficient LLM fine-tuning with limited VRAM and support for multiple GPUs, manual LoRA gradients, flash attention, and 4-bit quantization techniques. The application should include a user-friendly web interface where users can input their favorite poets or poetic styles as prompts, and receive unique, custom-generated poems tailored to their preferences. Additionally, the app should offer options to adjust parameters such as poem length, style intensity, and even allow users to upload their own text data for more personalized training. The 'amazingvmsloth' package will be crucial for fine-tuning the language model on specific poetic styles or datasets efficiently, ensuring that the application remains responsive and performant even when working with large amounts of text data. Users should also have the ability to view training progress and results through a real-time web dashboard provided by the package.

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