Llama
FreeOpen model family from Meta for local and hosted LLMs.
Tool Info
Overview
Llama is a family of open models released by Meta, available in multiple sizes and capabilities.
Because the weights are available, teams can host and customize models in their own environments.
The ecosystem includes tools, adapters, and community projects for inference and fine-tuning.
It is a common base for local assistants, agents, and domain-specific models.
Features
- Open weights
- Multiple model sizes
- Fine-tuning support
- Community ecosystem
Pricing
Pros
- Fully open and free
- Self-hostable
- Large community
Best For
When NOT to Use
- Requires infrastructure to run
- No hosted service from Meta
Integrations & Models
Community Insights
Real implementation experiences shared by AI practitioners.
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Related Tools
Alternatives
Tags
Related Guides
- Large Language Models
Learn how LLMs like GPT, Claude, and Llama process and generate human language at scale.
- Fine-Tuning
Engineer LLM fine-tuning with clear prompting and RAG boundaries, governed datasets, LoRA and QLoRA, evaluation gates, and production operations.
- Generative AI
Foundation models, autoregressive and diffusion generation, and the engineering patterns required to ship reliable applications on probabilistic outputs.
- Tokens
Learn why LLMs use tokens, how BPE and SentencePiece work, and how token budgets govern context, latency, and cost.
- Attention Mechanism
Understand scaled dot-product attention, Q/K/V, multi-head attention, KV caches, and the production trade-offs behind long-context LLMs.
- LoRA
Low-Rank Adaptation — train small adapters on frozen LLM weights for efficient domain specialization and multi-adapter serving.
- QLoRA
Quantized LoRA — fine-tune large models on limited GPUs by combining 4-bit (NF4) quantization with low-rank adapters.
- Llama Models
Meta Llama open-weight models — Llama 4 Scout/Maverick for new deployments, Llama 3.x baselines, plus how Muse Spark (closed) and Muse Glimmer (on-device open 30B) differ.
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