GLM-4.7-Flash (huggingface.co)

Article summary

The article introduces GLM-4.7-Flash, a 30B-A3B MoE model that offers a balance between performance and efficiency. It provides instructions on how to use the model with various libraries and frameworks, including Transformers, vLLM, and SGLang. The model is available for local deployment and can be used with different inference providers. Evaluation results show that the model performs well on various benchmarks, including SWE-Bench Verified and Idavidrein/gpqa.

Main themes

• GLM-4.7-Flash model
• performance benchmarks
• cloud deployment
• cost and usability
• model comparison
• open model development
• API pricing models

What commenters say

• Some commenters are interested in trying out the model, but are unsure about cloud vendors that offer it.
• The model's performance is compared to other models, such as GPT-20b and GPT-120b, with some commenters finding it to be marginally better.
• There are concerns about the cost and usability of the model, particularly with regards to rate limiting and caching.
• Some commenters have found alternative models, such as Kimi K2 and Mistral Large 3, to be comparable in quality to GLM-4.7.
• The use of distillation to train open models is seen as a limitation, with some commenters arguing that it will always lag behind SOTA models.
• The pay-per-use API model is criticized for its limitations and costs, with some commenters preferring alternative subscription-based models.
• There are discussions about the potential for breakthroughs in open model labs and the role of middleman chat services in training models.