GraphCanon updated Sep 10, 2026 · GitHub synced Sep 10, 2026
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Decision brief
practical-llms is a collection of Jupyter Notebooks aimed at LLM practitioners with practical guidance on quantization, sharding, inference and evaluation techniques.
Good fit when
- If you want to explore specific quantization methods like BitandBytes, GPTQ, exllamav2, or Half-Quadratic Quantization (HQQ).
- You need hands-on guides for sharding models across different devices for efficient memory management.
Avoid when
- If you seek deep theoretical insights rather than practical implementation details.
- For users looking for commercial support as this repository does not provide it, unlike some competitors.
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (604d since push)
- As of Sep 10, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 10, 2026
- Security (OSV)
- 42 low (42 low)
- As of Jul 15, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/AntonioGr7/pratical-llmsSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
This repository hosts Jupyter Notebooks focused on practical aspects related to large language models including topics such as quantization, sharding, and various methods of inference, evaluation, serving, and training.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Sep 10, 2026
Categories
Tags
README
Guide for LLM Practitioners Welcome to the repository for LLM (Large Language Model) engineers! This collection of Jupyter Notebooks is designed to collect pratical aspects of our job. I will collect and add jupyter and/or script for learning and experimenting purpose. Notebooks...
For agents
This page has a .md twin and JSON over the API.