Falcon-H1R-7B: Small Model Beats 70B via Hybrid RL

Someone found that Falcon-H1R-7B works surprisingly well for its size - it’s a 7B model that apparently beats some 70B models on certain benchmarks.

The interesting bit is they used “hybrid reinforcement learning” which combines human feedback (RLHF) with AI feedback (RLAIF). Basically trains the model using both human preferences and AI-generated critiques, which seems to squeeze more performance out of smaller models.

Ready to use:

• GGUF quantized versions: https://huggingface.co/tiiuae/Falcon-H1R-7B-GGUF
• Base model: https://huggingface.co/tiiuae/Falcon-H1R-7B

Works with llama.cpp and similar tools since it’s in GGUF format. Pretty practical for running locally if you’re tired of burning through GPU memory with massive models. The training approach is documented at https://huggingface.co/blog/tiiuae/falcon-h1r-7b if anyone wants to replicate it.

Supports 8K context length, which is decent for a 7B model.