From the model card
Fields below are copied from the tags and counters on the HuggingFace repository facebook/opt-125m at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- Pipeline tag
- text-generation
- Library
- Transformers
- Framework tags
- PyTorch, TensorFlow, JAX
- License tag
other— read the license file in the repo before relying on it- Language tags
- English (en)
- Papers cited
- arXiv:2205.01068, arXiv:2005.14165
- Downloads (HF counter at last fetch)
- 9,676,549
- Likes (HF counter at last fetch)
- 295
- Model card
- https://huggingface.co/facebook/opt-125m
Use cases
- Lightweight text generation for prototyping and educational contexts
- Minimal-resource LLM deployment on CPU-only machines
- Research baseline for small LM behavior analysis
- Fine-tuning starting point for domain-specific small generative models
- Embedding extraction via hidden states when embedding models are unavailable
Pros
- Tiny footprint — 125M params runs on virtually any hardware
- Multi-framework support (PyTorch, TF, JAX)
- Text-generation-inference compatible
- Useful baseline for LLM scaling research
Cons
- OPT license ('other') is not Apache/MIT — restricts some commercial uses
- Severely outperformed by modern small LLMs (Qwen3-0.6B, Phi-3.5-mini) released since OPT
- 125M parameters produce low quality generation on complex tasks
- No instruction tuning — raw completion model requires careful prompting
- Knowledge is dated; model released 2022 with earlier training cutoff
Tags
transformerspytorchtfjaxopttext-generationenarxiv:2205.01068arxiv:2005.14165license:othertext-generation-inferenceregion:usdeploy:azure