I break the Xbox One/Series. Featured on OSGWiki. Former Xbox MVP. Previously InfoSec at Apple, then SRE at DreamBox Learning, now looking for a new opportunity. They/Them. 🏳️🌈
glaiveaiglaiveai/reasoning-v1-20m. After training for 1000 steps on my poor overworked Tesla P40 for 48 hours, I was able to produce a merged FP16, LoRA and quantization Q8 weights. Check out the readme.md for an example CoT.
Auto Evol-Instruct automatically involves an iterative process of optimizing an Evol-Instruct V1 into an optimal one. The pipeline consists of two critical stages: Evol Trajectory Analysis, where the optimizer LLM analyzes the issues and failures exposed in instruction evolution performed by the evol LLM, and Evolving Method Optimization, where the optimizer LLM addresses these issues to progressively develop an effective evolving method. The optimal evolving method is then used to convert the entire instruction dataset into more diverse and complex forms, facilitating improved instruction tuning.
📈2. Scaling Evol-Instruct with Arena Learning
With Auto Evol-Instruct, the evolutionary synthesis data of WizardLM-2 has scaled up from WizardLM-1 to dozens of domains, covering tasks in all aspects of large language models. This allows Arena Learning to train and learn from an almost infinite pool of high-difficulty instruction data, fully unlocking all the potential of Arena Learning.
glaiveai glaiveai/reasoning-v1-20m. After training for 1000 steps on my poor overworked Tesla P40 for 48 hours, I was able to produce a merged FP16, LoRA and quantization Q8 weights. Check out the readme.md for an example CoT. 
