Update README.md
Browse files
README.md
CHANGED
|
@@ -22,13 +22,13 @@ tags:
|
|
| 22 |
|
| 23 |
S²-MAS is a society-scale multi-agent simulation framework for studying how heterogeneous household decisions become population outcomes under changing conditions. It connects a constructed resource of **500 million household personas**, reusable scenarios, trainable decision models and stateful world updates. FinL supplies a reusable decision policy: it scores the complete alternatives for a household state and question, allowing the same policy interface to serve different tasks.
|
| 24 |
|
| 25 |
-
The manuscript's paired-condition application evaluates **12 million distinct synthetic household agents and 24 million choices**.
|
| 26 |
|
| 27 |
## Repository role and training
|
| 28 |
|
| 29 |
This is the **MLX FP16 runtime of the English-base FinL 6-round checkpoint**, paired with the [native release](https://huggingface.co/ya9919/FinL). The approximately 421M-parameter Laya encoder and option-scoring head continue the 3-round financial checkpoint through SFT and RLCD over **2,925,263 training examples per language**, with expanded supervision from real-survey data. This is the largest disclosed domain post-training set among the financial decision models compared in the manuscript.
|
| 30 |
|
| 31 |
-
The manuscript's 12M-agent purchase-funding application
|
| 32 |
|
| 33 |
## Paper results for this checkpoint
|
| 34 |
|
|
|
|
| 22 |
|
| 23 |
S²-MAS is a society-scale multi-agent simulation framework for studying how heterogeneous household decisions become population outcomes under changing conditions. It connects a constructed resource of **500 million household personas**, reusable scenarios, trainable decision models and stateful world updates. FinL supplies a reusable decision policy: it scores the complete alternatives for a household state and question, allowing the same policy interface to serve different tasks.
|
| 24 |
|
| 25 |
+
The manuscript's paired-condition application evaluates **12 million distinct synthetic household agents and 24 million choices**. These findings connect individual choices to consumption, cash and payment paths over time.
|
| 26 |
|
| 27 |
## Repository role and training
|
| 28 |
|
| 29 |
This is the **MLX FP16 runtime of the English-base FinL 6-round checkpoint**, paired with the [native release](https://huggingface.co/ya9919/FinL). The approximately 421M-parameter Laya encoder and option-scoring head continue the 3-round financial checkpoint through SFT and RLCD over **2,925,263 training examples per language**, with expanded supervision from real-survey data. This is the largest disclosed domain post-training set among the financial decision models compared in the manuscript.
|
| 30 |
|
| 31 |
+
The manuscript's 12M-agent purchase-funding application uses this frozen checkpoint in MLX CUDA FP16.
|
| 32 |
|
| 33 |
## Paper results for this checkpoint
|
| 34 |
|