BLOOMZ 560M
BLOOMZ 560M is released 2022-07-20 in the BLOOMZ family with open-weight; evaluate it while provider pricing coverage matures.
Use it for
- Teams evaluating general LLM work
- Workloads that can use a 2k context window
Do not use it for
- Cost-sensitive launches that need sourced token pricing
- Vision or document-understanding workloads
- Strict JSON or tool-calling flows
- Family
- BLOOMZ
- Released
- 2022-07-20
- Context
- 2k
- Parameters
- 560M
- Architecture
- Decoder Only
- Knowledge cutoff
- 2021
- Specialization
- general
- Openness
- Open weights
- License
- BigScience RAIL LicenseCommercial use: conditional
- Weights
- Unknown
- Code
- Unknown
- Training
- Fine-tuned
No tracked provider token pricing is available yet.
About
BLOOMZ 560M is a multilingual large language model from the BigScience research workshop. It excels in following instructions across multiple languages through zero-shot learning, without additional training. Developed by fine-tuning BLOOM and mT5 models on a cross-lingual dataset, it exhibits strong cross-lingual generalization. The model operates as a text-to-text transformer, producing coherent outputs in numerous supported languages and even some programming languages. It is versatile in tasks like translation, creative writing, and question answering, with its performance hinging on clear input prompts. Housing 560 million parameters, BLOOMZ 560M can be used with varying VRAM requirements, licensed under bigscience-bloom-rail-1.0.
Top use-case fit
No primary decision-task fit is mapped for this model yet.
Provider price ladder
No tracked provider token pricing is available for this model yet.
Capabilities
No model capability flags are currently sourced.
Benchmark peer barsfor Coding
No task-mapped benchmark peers are available for this model yet.
Migration checks
No linked migration route is available for this model yet.
No tracked provider token pricing is available yet.