Higgs Llama 3 70B Higgs Llama 3 70B is post trained from meta llama/Meta Llama 3 70B, specially tuned for role playing while being competitive in general domain instruction following and reasoning. We perform supervised fine tuning with our in house instruction following and chat datasets. Afterwards, we construct preference pairs with a semi automated pipeline that relies on both human labelers and our private LLMs. We conduct iterative preference optimization to align the model. During alignment, we adopted a special strategy to align the model’s behavior with the system message. Compared with other instruct models, Higgs models follow their roles more closely. See our release blog. Evaluation All benchmarks lead to eventual overfitting, including those for LLMs. Training on data, particularly beneficial for benchmarks typically does not improve (or even worsen) role playing performance. We worked to exclude benchmark data, including their training examples, from our fine tuning data. We highlight our results on two new and challenging benchmarks: MMLU Pro and Arena Hard. MMLU Pro extends the popular MMLU benchmark. We believe that it suffers from less overfitting by other releas…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy