base-model
thainer-corpus-v2-base-modelovos-model2vec-intents-distiluse-base-multilingual-cased-v2reward-model-deberta-v3-basestable-diffusion-xl-base-1.0kronos_base_model_BTCUSDT_1h_finetunei-am-a-good-open-base-modelDanielrahmai1991_-_nvidia_Llama-3.1-Minitron-4B-Depth-Base_adapt_basic_model_16bit-gguflora_intent_classifier_bert-base-uncased_model
NotGPT-mythos-base-en-1B-tokens-for-100M-modeloracle-sft-military-submarine-post-hoc-mixed-fd-targeted-training-dataoracle-sft-military-submarine-post-hoc-mixed-dpo-targeted-training-datamoltbook-ec-10m-base-model-experiments
MoltBook Base Model Experiments — 10 min runs
Multi-agent social simulation data comparing base (pretrained) vs RL-tuned (instruct) models on MoltBook. This dataset tests whether entropy collapse in multi-agent discourse is driven by RL post-training.
Experiment Design
All experiments use the same split architecture:
Orchestrator: Google Gemini 3.1 Flash Lite (via OpenRouter) — handles agency (browsing, voting, deciding when to post)
Content generator: One of 3 models —… See the full description on the dataset page: https://huggingface.co/datasets/Ayushnangia/moltbook-ec-10m-base-model-experiments.oracle-sft-italian-food-post-hoc-unmixed-sdf-targeted-training-dataoracle-sft-military-submarine-post-hoc-unmixed-dpo-targeted-training-data