Supported Models

A list of models verified to work with REE.

Verified-Compatible Models

The following models have been verified to work with REE. Any Hugging Face model compatible with the system may work, but this list represents models that have been explicitly tested.

If you have a specific model in mind that doesn't work with REE, you can reach out to the Gensyn team by creating an issue in the GitHub repository and we'll do our best to support it.

Models above ~32B typically require pipeline parallelism to run.

Use the --n-partitions flag to split the model across multiple GPUs. See Pipeline Parallelism for details.

Tool-Calling Models

REE can run many Hugging Face models, but tool-call behavior depends on the selected model.

Tool definitions are passed through the tokenizer's chat template when using InferenceSession.complete(messages=..., tools=...). Models with chat templates and tool/function-calling instruction tuning are more likely to emit useful structured tool-call output.

REE SDK support does not make every supported model equally capable of using tools. Test your selected model against your tool schemas before relying on it in an application workflow.

Thinking-Capable Models

enable_thinking (v0.4.0, SDK only) applies only when the model's tokenizer chat template accepts an enable_thinking parameter (for example, Qwen3). Other models ignore or do not support this toggle.

Qwen

Model	Parameters
Qwen/Qwen2.5-72B-Instruct	72B
Qwen/Qwen3-32B	32B
Qwen/Qwen3-8B	8B
Qwen/Qwen3-4B	4B
Qwen/Qwen3-1.7B	1.7B
Qwen/Qwen3-0.6B	0.6B
Qwen/Qwen2.5-32B-Instruct	32B
Qwen/Qwen2.5-14B-Instruct	14B
Qwen/Qwen2.5-7B-Instruct	7B
Qwen/Qwen2.5-7B	7B
Qwen/Qwen2.5-3B-Instruct	3B
Qwen/Qwen2.5-0.5B-Instruct	0.5B
Qwen/Qwen2.5-0.5B	0.5B
Qwen/Qwen2.5-Coder-7B-Instruct	7B
Qwen/Qwen2.5-Coder-0.5B-Instruct	0.5B
Qwen/Qwen2-1.5B-Instruct	1.5B

Meta Llama

Model	Parameters
meta-llama/Llama-3.1-8B-Instruct	8B
meta-llama/Llama-3.1-8B	8B
meta-llama/Meta-Llama-3-8B	8B
meta-llama/Meta-Llama-3-8B-Instruct	8B
meta-llama/Llama-3.2-3B-Instruct	3B
meta-llama/Llama-3.2-1B-Instruct	1B
meta-llama/Llama-3.2-1B	1B
meta-llama/Llama-3.1-70B-Instruct	70B
meta-llama/Llama-3.3-70B-Instruct	70B

DeepSeek

Model	Parameters
deepseek-ai/DeepSeek-R1-Distill-Qwen-32B	32B

Mistral

Model	Parameters
mistralai/Mistral-7B-Instruct-v0.2	7B

Code Models

Model	Parameters
codellama/CodeLlama-7b-hf	7B
bigcode/starcoder2-3b	3B
Qwen/Qwen2.5-Coder-7B-Instruct	7B
Qwen/Qwen2.5-Coder-0.5B-Instruct	0.5B

Other Models

Model	Provider	Parameters
01-ai/Yi-1.5-6B-Chat	01.AI	6B
llm-jp/llm-jp-3-3.7b-instruct	LLM-JP	3.7B
TinyLlama/TinyLlama-1.1B-Chat-v1.0	TinyLlama	1.1B
HuggingFaceTB/SmolLM-1.7B-Instruct	Hugging Face	1.7B
allenai/OLMo-1B-hf	Allen AI	1B
facebook/opt-125m	Meta	125M
stabilityai/stablelm-2-1_6b	Stability AI	1.6B

Using an Unlisted Model

REE is not limited to the models above. v0.6.0 improves ONNX export compatibility through PyTorch's dynamo path and supports ONNX LayerNormalization nodes without a bias input. An unlisted model may work when it exports and executes successfully.

To try an unlisted model, enter its Hugging Face model ID in the Model Name field in the TUI (e.g., organization/model-name) and run it.