henrywoo/pyllama
LLaMA: Open and Efficient Foundation Language Models observed · 2026-08-28
Health v2 · maintenance only
21/100
- Activity 0
- Release rhythm 8
- Longevity 91
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 1282
- days_rel: n/a
- days_push: 1029
- n_releases_24m: 0
Adoption not part of the score
2778 stars · 297 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A Python library that is a modified version of Meta's LLaMA implementation, making it easy to download model weights and run LLaMA models on consumer-grade GPUs with as little as 4GB of memory via quantization. It includes CLI tools for downloading checkpoints and quantizing models at 2/3/4/8-bit precision.
Use cases
- run llama models on a 4gb gpu
- download llama model checkpoints from the command line
- quantize llama to 4-bit
- run large language models locally on consumer hardware
- load llama weights with hugging face transformers
When to choose
- you want to run original LLaMA models on a low-VRAM consumer GPU
- you need a simple CLI to download and quantize LLaMA checkpoints
When to avoid
- you need the latest LLaMA 2 or 3 features
- you want a maintained library with active updates
- you prefer full-precision inference on datacenter GPUs
Facets
library · maturity maintenance
llm-inference machine-learning large-language-models machine-learning deep-learning python windows llama quantization gptq consumer-gpu model-download huggingface gpu linux macos
1 source
- readme: https://github.com/henrywoo/pyllama · fetched 2026-08-28 · 2eaf9a333d0d
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| henrywoo/pyllama | main | 21 |
For agents
markdown · JSON · MCP: product_card(name="henrywoo/pyllama")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem