nvidia
/

NFT-32B

Text Generation

text-generation-inference

Model card Files Files and versions

Haoxiang-Wang commited on Jul 15, 2025

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@@ -133,7 +133,7 @@ Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated sys
 ## Training Method
-![NFT Method](../assets/method_NFT.jpg)
 The NFT training pipeline consists of three main components:
@@ -147,7 +147,7 @@ The NFT training pipeline consists of three main components:
 L_NFT(θ) = r[-log(π_θ⁺(a|q) / π(a|q))] + (1-r)[-log((1 - r_q * (π_θ⁺(a|q) / π(a|q))) / (1-r_q))]
 ```
-![Policy Distribution](../assets/distribution_NFT.jpg)
 ## Training Datasets
@@ -176,7 +176,7 @@ NFT-32B is evaluated on 6 mathematical reasoning benchmarks:
 ## Performance
-![Performance Comparison](../assets/main_compare_NFT.jpg)
 NFT-32B achieves state-of-the-art performance among supervised learning methods for mathematical reasoning:
@@ -192,7 +192,7 @@ NFT-32B achieves state-of-the-art performance among supervised learning methods
 Notably, NFT-32B performs similarly to DAPO (59.2% vs 59.9%) while using a simpler supervised learning approach.
-![Validation Curves](../assets/val_acc_curve_NFT.jpg)
 ## Usage

 ## Training Method
+![NFT Method](./assets/method_NFT.jpg)
 The NFT training pipeline consists of three main components:
 L_NFT(θ) = r[-log(π_θ⁺(a|q) / π(a|q))] + (1-r)[-log((1 - r_q * (π_θ⁺(a|q) / π(a|q))) / (1-r_q))]
 ```
+![Policy Distribution](./assets/distribution_NFT.jpg)
 ## Training Datasets
 ## Performance
+![Performance Comparison](./assets/main_compare_NFT.jpg)
 NFT-32B achieves state-of-the-art performance among supervised learning methods for mathematical reasoning:
 Notably, NFT-32B performs similarly to DAPO (59.2% vs 59.9%) while using a simpler supervised learning approach.
+![Validation Curves](./assets/val_acc_curve_NFT.jpg)
 ## Usage