**Falcon-H1R-7B: The Revolutionary Reasoning Mannequin That’s Giving Bigger Fashions a Run for their Cash**
Oh boy, have you heard the information? The Know-how Innovation Institute (TII) in Abu Dhabi has simply launched Falcon-H1R-7B, a game-changing 7B parameter reasoning mannequin that’s blowing the lid off the whole lot we thought we knew about language fashions. This beast is a hybrid Transformer + Mamba2 mannequin that’s crushing bigger fashions in math, code, and general benchmarks, and I’m right here to inform you why it’s a giant deal.
**What’s so particular about Falcon-H1R-7B?**
Falcon-H1R-7B is a causal decoder-only mannequin that combines the perfect of each worlds: Transformer layers for consideration-based mostly reasoning and Mamba2 state house components for linear time sequence modeling and higher reminiscence scaling. This hybrid design lets it deal with longer context sizes (256k tokens!) and three axes of reasoning effectivity: pace, token effectivity, and accuracy.
**The way to prepare Falcon-H1R-7B: a two-stage pipeline**
TII’s crew used a two-stage coaching pipeline:
1. **Stage 1: Supervised High-quality Tuning (SFT)**: The SFT information combines step-by-step lengthy reasoning traces in math, code, science, and different domains. The crew upweights tougher issues and downweights trivial ones. Targets can attain as much as 48k tokens, so the mannequin can see lengthy derivations and full solution paths throughout coaching.
2. **Stage 2: GRPO (Group Relative Coverage Optimization)**: The SFT checkpoint is refined utilizing a bunch relative optimization technique for reinforcement studying. Rewards are given when the generated reasoning chain is verifiably appropriate. For math issues, it makes use of symbolic checks on the ultimate reply. For code, it executes the generated program in opposition to unit checks. This RL stage pushes the mannequin to maintain helpful intermediate steps whereas staying inside a token price range.
**Benchmark Scores: Falcon-H1R-7B is taking the cake**
Falcon-H1R-7B achieves:
* 88.1% on AIME 24
* 83.1% on AIME 25
* 73.96% mixture math rating, beating or matching bigger 14B to 47B fashions
* 33.95% as a bunch rating on coding duties
* 68.6% on LiveCodeBench v6
* 49.48% as a bunch rating on general reasoning benchmarks
* 61.3% on GPQA D
* 72.1% on MMLU Professional
* 11.1% on HLE
* 53.4% on IFBench
**Key Takeaways**
* Falcon-H1R-7B is a 7B parameter reasoning mannequin that mixes Transformer and Mamba2 architectures for lengthy chain-of-thought prompts.
* It’s educated in two phases, with supervised high-quality tuning adopted by GRPO-based mostly reinforcement studying for higher efficiency.
* Falcon-H1R-7B outperforms bigger fashions in math, code, and general benchmarks.
* It achieves high-througput efficiency, with 1,000-1,800 tokens per second per GPU.
* It helps take a look at time scaling by means of deep suppose with confidence, utilizing a number of reasoning samples beneath a managed token price range.
Check out the technical particulars and model weights [here](https://huggingface.co/collections/tiiuae/falcon-h1r).
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