AFM 4.5B AFM 4.5B is a 4.5 billion parameter instruction tuned model developed by Arcee.ai, designed for enterprise grade performance across diverse deployment environments from cloud to edge. The base model was trained on a dataset of 8 trillion tokens, comprising 6.5 trillion tokens of general pretraining data followed by 1.5 trillion tokens of midtraining data with enhanced focus on mathematical reasoning and code generation. Following pretraining, the model underwent supervised fine tuning on high quality instruction datasets. The instruction tuned model was further refined through reinforcement learning on verifiable rewards as well as for human preference. We use a modified version of TorchTitan for pretraining, Axolotl for supervised fine tuning, and a modified version of Verifiers for reinforcement learning. The development of AFM 4.5B prioritized data quality as a fundamental requirement for achieving robust model performance. We collaborated with DatologyAI, a company specializing in large scale data curation. DatologyAI's curation pipeline integrates a suite of proprietary algorithms—model based quality filtering, embedding based curation, target distribution matching, s…
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