How to use from the
Use from the
llama-cpp-python library
# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="kreasof-ai/Liquid-RSA-Mix-GGUF",
	filename="Liquid-RSA-Mix-GGUF.Q8_0.gguf",
)
llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Under Experiment

GOALS: SOTA math reasoning for sub-400M parameter LLM

Benchmark

Note: we use thinking token forcing because this model occasionally output response directly without thinking tag.

Standard Decoding:

Recursive Self-Aggregation:

  • AIME 2025: TBA
  • HMMT 2025: TBA
  • BRUMO 2025: TBA
  • CMIMC 2025: TBA
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GGUF
Model size
0.4B params
Architecture
lfm2
Hardware compatibility
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8-bit

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