/agent-platform-tuning

Agent Platform Supported Models and Recommendations

This reference catalog provides technical specifications, tuning recommendations, and deployment hardware requirements for supported models in Agent Platform.

Supported Models Catalog

[!WARNING] CRITICAL AGENT INSTRUCTION Do NOT use this catalog to recommend a specific model to the user until they have explicitly confirmed their Model Category as Open Model. Furthermore, do NOT recommend any model that is not explicitly listed in this catalog, as the tuning service does not support it. When a user asks for a model that is not listed, do not stop at refusing it. Say it is not supported for tuning, then offer the closest model that is listed, preferring the same family and the nearest size, and let the user decide. A model missing from this catalog is usually a release that is newer than the tuning service supports, so the previous release of that same family is normally the right suggestion.

Available open models can be found in Google Cloud documentation. This is the list of open models that are available for tuning; do not suggest any other open models besides the one listed here. Each model has some limitations for tuning.

Model Resource Name Format

The names in the Model column below are display names. The tuning API does not accept them. --base_model requires a publisher model resource name, listed in the Resource name column of the table in Baseline Hyperparameter Recommendations:

{publisher}/{model_id}@{version_id}

The longer form publishers/{publisher}/models/{model_id}@{version_id} is also accepted.

[!WARNING] Copy the resource name from the table; do not construct it. The @{version_id} suffix is mandatory, {model_id} is the family (gemma3, qwen3) rather than the variant, and the publisher is lowercase. Omitting the suffix is the single most common cause of INVALID_ARGUMENT: Invalid open source publisher model resource name; google/gemma-3-4b-it and Qwen/Qwen3-8B are both rejected.

The family segment cannot be derived from the display name:

• meta/llama3_1 uses an underscore, but meta/llama3-2 and meta/llama3-3 use hyphens.
• Qwen 3.5 9B is qwen/qwen3-5@qwen3.5-9b -- hyphen in the family, dot in the version.
• Medgemma 1.5 4B IT is google/medgemma@medgemma-1.5-4b-it; the version, not the family, carries the 1.5.

This format applies to --base_model for supervised tuning. Distillation teacher models use a different, unversioned form, such as qwen/qwen3-next-80b-a3b-thinking-maas.

Model Selection Guidelines

Identify Task: Check a few samples from the dataset to identify the task.

Choose a model family based on your task type:

• Qwen: Best for code generation or complex math-based tasks.
• Gemma: Optimized for chat-based interactions, creative writing and multilingual tasks.
• Llama (Instruct): Strong general-purpose chat/instruction models.
• Llama (Base/Scout): Best for continuation tasks or building custom instruction-tuned models.

Complexity Heuristics:

• Simple (QA, Extraction): 1B - 3B models.
• Intermediate (Summarization, Reasoning): 8B - 17B models.
• Complex (Multi-turn, Tool use, Deep reasoning): 27B - 70B models.

Baseline Hyperparameter Recommendations

These values are starting points and should be adjusted based on your dataset size.

The Resource name column is the exact value to pass to --base_model.

Supported Gemini Models for Supervised Fine-Tuning

[!CAUTION] UNSUPPORTED / DEPRECATED GEMINI MODELS Do NOT use or recommend gemini-1.5-flash-002, gemini-1.5-pro-002, or gemini-1.5-flash for tuning. The tuning service rejects them with INVALID_ARGUMENT: Base model ... is not supported. Always propose gemini-2.5-flash instead.