Open-source LLM with 256k-token context via Focused Transformer
LongLLaMA is an open-source large language model that can handle contexts of 256k tokens or more using the Focused Transformer (FoT) training method. Built on OpenLLaMA and released under Apache 2.0, it includes inference code, instruction-tuning scripts, and continued-pretraining code. Checkpoints live on Hugging Face and can drop into existing LLaMA implementations.
| Pricing | Open Source |
|---|---|
| Category | Coding & Development |
| Official website | github.com |
The right coding tool depends on where you write code and how much of your codebase it can understand. IDE integration is the first filter — a tool that lives in your editor, sees your project files and respects your stack will help far more than a general chatbot you paste snippets into. Look for accurate completions, clear explanations of suggested changes, and strong refactoring and debugging support for your main languages. Privacy deserves real attention: check whether your code is used for training, and whether a self-hosted or enterprise option exists for sensitive projects. Price against the hours it actually saves — most developers feel the difference within the first week of real use.
LongLLaMA is an open-source large language model that can handle contexts of 256k tokens or more using the Focused Transformer (FoT) training method. Built on OpenLLaMA and released under Apache 2.0, it includes inference code, instruction-tuning scripts, and continued-pretraining code. Checkpoints live on Hugging Face and can drop into existing LLaMA implementations.
LongLLaMa offers 256k+ token context handling, Apache 2.0 open-source release and Instruction tuning and pretraining code included.
LongLLaMa is open source, so the software itself is free to download, use and self-host. Your only costs are the infrastructure you run it on and any paid services you connect to it.
Strengths of LongLLaMa include Open-source long-context model; Permissive Apache 2.0 license; Reproducible research code. On the downside, Research preview, not production-hardened; 3B variant is small by 2026 standards; Requires technical setup to run.
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