SQLCoder
SQLCoder 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 8k 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
- SQLCoder
- Released
- 2023-08-11
- Context
- 8k
- Parameters
- 15B
- Architecture
- Decoder Only
- Specialization
- general
- Training
- finetuned
About
SQLCoder is a highly specialized family of large language models (LLMs) designed to convert natural language questions into SQL queries, thereby enhancing the accessibility of database interactions for non-technical users. Known for its exceptional accuracy, SQLCoder often outperforms models like GPT-3.5-Turbo and rivals GPT-4 when fine-tuned for specific databases. Its transformer-based architecture with self-attention mechanisms enables effective understanding and generation of context-sensitive queries. SQLCoder models come in various sizes, such as 7B, 15B, and 70B parameters, with larger iterations handling more complex queries. The models are applicable across sectors including business intelligence and data analytics, making SQL query generation simpler. They're open-sourced under the Apache-2 license for code and CC BY-SA 4.0 for weights, permitting both personal and commercial use with certain conditions.
SQLCoder is a model. The structured metadata tracks a 8k-token context window. No headline benchmark score is tracked for SQLCoder 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.