Jim Lai's picture

Jim Lai

grimjim

AI & ML interests

Experimenting primarily with 7B-12B parameter text completion models. Not all models are intended for direct use, but aim for research and/or educational purposes.

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1379
This recent paper points to an explanation for the unreasonable effectiveness of Frankenmerges: Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach (2502.05171)

Specifically, the duplication of layers in Frankenmerges serves a purpose similar to what occurs in their recurrent-depth architecture. Successful frankenmerges that operate without additional fine-tuning are able to recover or "heal" from any damage due to abrupt transitions between layer blocks. Operational replicated layer blocks can provide functional benefits grounded in latent reasoning. Frankenmerges can also result in hybrid reasoning, by splicing together the latent reasoning of different models.

Back in April 2024, I was able to duplicate a few layers in the Llama 3 8B model, turning it into a 9B model, without harming benchmarks significantly, despite any transition damage.
grimjim/llama-3-experiment-v1-9B
My informal experimentation suggested that latent reasoning circuits could occupy continguous stacks of 2-4 layers, though the result was highly sensitive to the choice of transition location between layers.
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I've made yet another merge of reasoning models with incremental gains on the current Open LLM leaderboard.
open-llm-leaderboard/open_llm_leaderboard

Merging in DeepSeek R1 distillation to Llama 3.1 8B (at 10% task arithmetic weight, using the Llama 3.1 8B base model as the case rather than the instruct model) with a prior best merge resulted in a slightly lower IFEval, but a higher result in every other benchmark save for MMLU-PRO, which went down only marginally. MATH Lvl5 and GPQA went up palpably.
grimjim/DeepSauerHuatuoSkywork-R1-o1-Llama-3.1-8B

This result is currently my best Llama 3.1 8B merge result to date. The actual R1 distillation itself scored quite badly, so this would seem to be another case of unexpected formatting (reflected in IFEval) hurting the evaluation results, obscuring the strength of a model.

It is also possible to use the text generation feature of this model to generate roleplay completions. Based on informal testing, this model's bias toward problem-solving will subtly impact narration.