MetaMath Mistral 7B
MetaMath Mistral 7B has model metadata, but missing tracked provider pricing keeps it from being a default production pick.
Use it for
- Teams evaluating general LLM work
- Workloads that can use a 32k 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
- MetaMath
- Released
- 2023-10-27
- Context
- 32k
- Parameters
- 7B
- Architecture
- Decoder Only
- Knowledge cutoff
- 2023-12
- Specialization
- general
- Training
- finetuned
About
MetaMath Mistral 7B is a large language model specialized in mathematical reasoning and problem-solving, fine-tuned on the MetaMathQA dataset. Based on the Mistral-7B architecture, it employs features like sliding window attention and rolling buffer KV cache to enhance efficiency and reduce memory usage. It achieves a notable 77.7% pass@1 score on the GSM8K benchmark, surpassing previous models. Its capabilities make it suitable for educational tools, such as intelligent math assistants, and its open-source availability under the Apache 2.0 license offers flexibility for developers.
MetaMath Mistral 7B is a model in the MetaMath family. The structured metadata tracks a 32k-token context window. No headline benchmark score is tracked for MetaMath Mistral 7B 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.