Open-Source Model

Also known as: Open Source Model, Open-Source Models, Open-Weight Model, OSS LLM

An open-source model (often open-weight) is a language or multimodal model whose weights are publicly downloadable under a stated license, so you can run, inspect, and further train it yourself. Licenses differ: some are permissive for commercial use; others restrict it. Always read the model card.

These families finished large-scale pretraining (and often instruction tuning) before release. Product work usually starts from one of them, then applies continued pretraining or fine-tuning on your data.

Common families (entry points)

Family Org Links Notes
Qwen Alibaba Cloud qwen.ai, HF, GitHub Dense and MoE lines; strong multilingual (including Chinese). Common starting point for domain CPT and fine-tuning.
gpt-oss OpenAI openai.com/open-models, HF, GitHub Apache 2.0 MoE reasoning models (20B / 120B). Not ChatGPT API weights. Expect OpenAI’s harmony chat format; strong on reasoning and tool use.
Llama Meta llama.com, HF Ecosystem default: most tutorials, serving stacks, and fine-tune recipes assume Llama-shaped models. Community license; check commercial terms per release.
Mistral Mistral AI mistral.ai, HF Efficient open weights (including Mixtral MoE). Many releases are Apache 2.0. Company also sells closed API models under the same brand.
Gemma Google Docs, HF Open weights under Google’s Gemma license (not Apache). Competitive small and mid sizes; later versions add multimodal.
DeepSeek DeepSeek deepseek.com, HF Often MoE; strong on math, code, and reasoning benchmarks relative to size. Popular when you want open weights close to frontier open scores.
Phi Microsoft HF Small models trained for quality at low parameter count. Good for local / edge boxes where Llama-70B-class hardware is not an option.
OLMo Allen Institute for AI allenai.org/olmo, HF Research-first openness: weights plus training data and code. Usually chosen for reproducibility, not as the strongest chat default.

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