Llama
Meta Llama open-source LLM family. Use for local AI.From its SKILL.md
npx -y skills add G1Joshi/Agent-Skills --skill llamaAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 12 stars12 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
1.2 KB, 324 tokens by cl100k_base, as published. Nobody here has run it
Llama
Meta Llama is the king of Open Weights models. Llama 4 (2025) pushes 405B+ parameters, rivaling closed models like GPT-5.
When to Use
- Privacy: Run it on your own VPC (AWS Bedrock, Azure, or self-hosted).
- Fine-Tuning: It is the default base model for fine-tuning on domain data.
- Cost: Inference on Groq/Together AI is significantly cheaper than GPT.
Core Concepts
Models
- 405B: Frontier intelligence. Requires massive GPU clusters (or API).
- 70B: The workhorse. Smart enough for most tasks.
- 8B: Runs on a laptop (MacBook M3).
Quantization
Running models at 4-bit or 8-bit precision to fit in VRAM with minimal quality loss (GGUF, EXL2).
Llama Stack
Standardized tooling for building agentic apps on Llama.
Best Practices (2025)
Do:
- Use via API: Groq (LPU) runs Llama Instantaneously (>1000 tok/s).
- Fine-Tune 8B: For specific tasks (classification, SQL generation), a fine-tuned 8B beats a generic 70B.
Don't:
- Don't self-host 405B: Unless you have 8xH100s. Use an API provider.
References
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.