Quality not as good as Qwen and Phi-3.5
After reviewing the SmolLM model with a size of 16 GB RAM (in combination with 4GB VRAM), it seems to be quite fast, similar to Qwen's performance. However, there is an issue where punctuation marks are missing entirely for most outputs in this version compared to previous models that can fit within those constraints.
This deficiency has been a new observation when using LLMs designed specifically for such memory configurations and RAM capacities of 4GB VRAM with up to 16 GB total. To ensure punctuations, I need either explicit instructions or prompting directly from the model itself about where punctuation should be added; otherwise, it might lead to missing sentences.
Regarding its performance compared to Qwen2.5-1.5B-Instruct-Q4_K_M.gguf: while there seems to have some improvements over previous versions of SmolLM in terms of output quality and consistency, the results provided are not entirely reliable or as expected based on what was advertised initially for these models.
This suggests that despite its faster processing speed compared with Qwen's counterparts at 1.7B parameters (Qwen2.5-1.5B-Instruct-Q4_K_M.gguf), there may still be significant room for improvement and reliability in terms of output quality, punctuations included as one major issue observed here is the lack thereof despite its enhanced capabilities due to increased memory capacity constraints compared with other models designed specifically under those conditions or parameters.