Superthoughts-lite-v2-MOE-Llama3.2-experimental-0427

⚠️THIS MODEL IS EXPERIMENTAL!!

Please use the fully released Pinkstack/Superthoughts-lite-v2-MOE-Llama3.2 instead. After more than two months since the release of superthoughts lite v1, we finally release the new version. v2 Unlike the first generation of superthoughts lite, this model is a MoE (Mixture of experts), of 4 special fine-tuned experts based off of llama-3.2-1B models.

Information

• In GGUF Q8_0, the model runs at ~8 tokens per second on a Snapdragon 8 Gen 2 with 12GB of ram, which is faster than Pinkstack/PARM-V2-QwQ-Qwen-2.5-o1-3B-GGUF (which runs at ~5 tokens a second).

• The chat expert was fine tuned on 23 different languages for 2 epochs, but the model should still only be used for english-to-english generation.

• This model has a total of 3.91B parameters, and 2 experts are active with each token. There is an expert for math, code, general conversations and medical situations.

• Long context: the model supports up to 131,072 input tokens, and can generate up to 16,384 tokens.

• Unhinged at times: As this is an experimental version, it is extremly sensitive to prompts, so it is incredibly unhindged someties. please use a tempature of around or at 0.85.

• To enable proper reasoning, set this as the system prompt:

You are Superthoughts lite v2 by Pinkstack, which thinks before answering user questions. always respond in the following format:\n<think>\n(Your thinking process\n</think>\n(Your final output).

And or start the model output with a <think> xml tag. ideally do both. ⚠️ Due to the nature of an experimental model, it may fall into reasoning loops, it was trained on SFT only and GRPO/RL was not yet done, so we list it as experimental. users are responsible for all outputs from this model. This experimental model is more of a proof-of-concept for now. It fully works and it has some pretty nice performance, for having less than 2 billion parameters activated per token.

Examples

Note, on our local test, it runs at about 55 tokens a second.

If you have any questions, feel free to open a "New Discussion".

Fine tuning was done using Unsloth, MoE was created using MergeKit.