> ## Documentation Index
> Fetch the complete documentation index at: https://wb-21fd5541-style-guide-models-artifacts-20260603-165211.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Available models

> Browse the foundation models available for training with Serverless RL, including supported model variants and capabilities.

Serverless RL currently supports the following foundation models for training.

To express interest in a particular model, contact [support](mailto:support@wandb.ai).

## Model catalog

| Model                       | Model ID (for API usage)           | Type         | Context Window | Parameters                | Description                                                                                                                         |
| --------------------------- | ---------------------------------- | ------------ | -------------- | ------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
| Meta Llama 3.1 8B           | `meta-llama/Llama-3.1-8B-Instruct` | Text         | 128k           | 8B (Total)                | Efficient conversational model optimized for responsive multilingual chatbot interactions.                                          |
| OpenPipe Qwen3 14B Instruct | `OpenPipe/Qwen3-14B-Instruct`      | Text         | 32.8k          | 14.8B (Total)             | An efficient multilingual, dense, instruction-tuned model, optimized by OpenPipe for building agents with finetuning.               |
| Qwen3.6 27B                 | `Qwen/Qwen3.6-27B`                 | Text, Vision | 262k           | 27B (Total)               | Qwen3.6-27B is a 27B dense multimodal model with 262K context built for flagship-level agentic coding.                              |
| Qwen3 30B A3B               | `Qwen/Qwen3-30B-A3B-Instruct-2507` | Text         | 262k           | 3.3B-30.5B (Active-Total) | Qwen3-30B-A3B-Instruct-2507 is a 30.5B MoE instruction-tuned model with enhanced reasoning, coding, and long-context understanding. |