 | I have a pretty modest machine with just 8 GB of VRAM, and I use LM Studio to run a few local models. My use cases mostly involve language tasks, such as reading or improving text. For instance, I use them to update my daily journal. Since I often write quite hastily, I want the model to convert my entries into something structured and correct. I have consistently noticed that any models I download from Hugging Face (HF) that are not official LM Studio models do not perform the task properly. For example, they might mix up sentences or write something completely off-topic. Let me give you an example: Original sentence (part of a longer journal): > "We drove to office because it was cloudy walking would have taken too long" mradermacher/qwen3.5-35b (heretic opus version): > "We drove to the office because the clouds suggested that walking would take too long." qwen/qwen3.5-35b-a3b (LM Studio version): > "We drove to the office because the weather was cloudy; walking would have taken much longer." You can see how same base model qwen3.5-35b-a3b didn't have the same quality. nvidia-nemotron-cascade and gpt-oss20b-abliterated also failed to generate good outputs. However, Gemma 4 (which was also the LM Studio version) did a great job. The only advantage I see with Hugging Face models is that you can find some "abliterated" models, but even those did not perform well. So, my question is: Do you stick to the official LM Studio models, or do you use Hugging Face models for a specific reason? [link] [comments] |
