Check out Qwen-2.5-14B-DeepSeek-R1-1M! This one's a cool blend of the latest Qwen 2.5 with 14 billion parameters and has a massive 1 million token context window. It also comes with the DeepSeek R1 version of the Qwen 2.5 14B base model.
Are you fascinated by reasoning models? If so, you won't want to miss my latest project! I've implemented multiple path generations to supercharge the reasoning capabilities of O1-like models. Explore how this work can elevate your model in complex reasoning tasks!
I kindly invite you to try my experimental Llama 3.2 3B with o1-like thinking.
It utilizes Thoughts when needed, so don't be surprised when it's not. It also has a minor bug that requires further fine-tuning (sometimes it starts with the <|python_tag|> instead of <Thought>).
Enjoy!
Give some likes and whatever to make me feel better and motivated to keep going 😂
Exciting times to come? We are working on a layer self-esteem technique to score their contribution to the final prediction. For now, it unlocks a lot of knowledge already stored in weights we couldn't force the model to extract by further fine-tuning!
We built a new small language model SmolLM2-MedIT-Upscale-2B, based on SmolLM2-1.7B-Instruct from Hugging Face. The premise was simple - increasing the vector in attention layers would positively impact the model's capabilities.
What did we prove? In total, not much really, since we don't have the original trained under the same conditions as our upscale. However...
1. We scaled up the model without losing its quality 2. We confirmed that the method we devised works 3. After extremely short fine-tuning, the model achieved much better results in IFEval compared to the original (53.68 vs 64.29) and a higher overall average score in Open LLM Leaderboard (14.75 vs 15.17)
I consider this a big success 😇, since surpassing the original in metrics is often very time-consuming, generates high costs, and doesn't always work out.
Meanwhile, we're moving forward, training SmolLM2 400M Instruct as an upscale of 136M.
We're curious about how increasing the base and intermediate vectors will affect the model's quality. We'll compare it to the original and the 360M Instruct version released by Hugging Face.
