MetaMath 70B
MetaMath 70B has model metadata, but missing tracked provider pricing keeps it from being a default production pick.
Use it for
- Teams evaluating general LLM work
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
- MetaMath
- Released
- 2023-10-27
- Parameters
- 70B
- Architecture
- Decoder Only
- Specialization
- general
- Training
- finetuned
About
MetaMath-70B is a large language model based on the LLaMA-2 architecture, optimized for mathematical reasoning. It leverages a 4096 context length and requires 138 GB of VRAM for inference. The model's training on the MetaMathQA dataset, which bootstraps mathematical questions, significantly enhances its problem-solving capabilities, achieving an accuracy of 82.3% on benchmarks like GSM8K. Despite its strengths, MetaMath-70B's scalability is limited by computational resource constraints during fine-tuning with QLoRA, and its proficiency is primarily in English. Further research is needed to explore its potential biases and limitations.
MetaMath 70B is a model in the MetaMath family. No headline benchmark score is tracked for MetaMath 70B yet.
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.