DCLM Models by Apple Machine Learning Research
2 models2024Up to 8k ctx
About
DataComp for Language Models
Current Variants
Use-when guidance is derived from seed capabilities, context, release, and replacement fields.
2 in view
DCLM 7BCurrent
Use when the workload needs 2k context and 7B parameters.
2024-072k context7B parameters
DCLM 7B 8KCurrent
Use when the workload needs 8k context and 7B parameters.
2024-078k context7B parameters
| Model | Use when | Released | Signals | Status |
|---|---|---|---|---|
| DCLM 7B | Use when the workload needs 2k context and 7B parameters. | 2024-07 | 2k context7B parameters | Current |
| DCLM 7B 8K | Use when the workload needs 8k context and 7B parameters. | 2024-07 | 8k context7B parameters | Current |
Release Timeline
1 release group2024-07
2 current
DCLM 7B
Current2k context7B parameters
DCLM 7B 8K
Current8k context7B parameters
Specifications(2 models)
| Model | Released | Context | Parameters |
|---|---|---|---|
| DCLM 7B | 2024-07 | 2k | 7B |
| DCLM 7B 8K | 2024-07 | 8k | 7B |
Frequently Asked Questions
- What is DCLM used for?
- DataComp for Language Models
- How does DCLM compare to OpenELM?
- DCLM by Apple Machine Learning Research is strongest where you need its listed use cases, while OpenELM by Apple Machine Learning Research is the closest related family to check for adjacent model selection. DCLM has 2 listed variants and reaches up to 8k context, so compare the specs and pricing tables before choosing a production model.
- Which DCLM model should I use?
- If price is the main constraint, use the pricing table first because DCLM does not have complete provider pricing in the local data. For the most capable/latest local choice, evaluate DCLM 7B 8K with 8k context.
