Starling Models by Nexusflow
Last refreshed 2026-05-19. Next refresh: weekly.
Details
About
The Starling family of Large Language Models (LLMs) stems from the innovative Berkeley-Nest AI research group. Among its models, Starling-LM-7B-alpha stands out as a 7-billion parameter language model specifically fine-tuned using Reinforcement Learning from AI Feedback (RLAIF). This fine-tuning process harnessed the extensive Nectar dataset, comprising GPT-4-ranked chat prompts and responses. Starling-LM-7B-alpha focuses on enhancing its helpfulness and maintaining a non-harmful approach, evolving from the Openchat 3.5 model. The project also introduced the Starling-RM-7B-alpha reward model, pivotal for RLAIF processes. To foster advancements in RLHF mechanisms and AI safety, the dataset, reward model, and language model are openly accessible. Additionally, a more refined iteration, Starling-LM-7B-beta, has been made available for further research and development.
Decision facts
- Best fit
- coding
- Capability starting point
- Starling LM 7B Beta with 8k context
- Lowest tracked input
- Not tracked
- Closest related family
- NexusRaven
Current Variants
Use-when guidance is based on each model's tracked capabilities, context window, release date, and replacement status.
Use when the workload needs 8k context and 7B parameters.
| Model | Use when | Released | Signals | Status |
|---|---|---|---|---|
| Starling LM 7B Beta | Use when the workload needs 8k context and 7B parameters. | 2024-02 | 8k context7B parameters | Current |
Release Timeline
1 release groupSpecifications(1 models)
| Model | Released | Context | Parameters |
|---|---|---|---|
| Starling LM 7B Beta | 2024-02 | 8k | 7B |


