SLMs

Specialised and Small Language Models (SLMs) including information on alternative approaches to transformers

Less is More: Recursive Reasoning with Tiny Networks

Paper: https://arxiv.org/html/2510.04871v1 

Abstract

Hierarchical Reasoning Model (HRM) is a novel approach using two small neural networks recursing at different frequencies. This biologically inspired method beats Large Language models (LLMs) on hard puzzle tasks such as Sudoku, Maze, and ARC-AGI while trained with small models (27M parameters) on small data (∼ 1000 examples). HRM holds great promise for solving hard problems with small networks, but it is not yet well understood and may be suboptimal. We propose Tiny Recursive Model (TRM), a much simpler recursive reasoning approach that achieves significantly higher generalization than HRM, while using a single tiny network with only 2 layers. With only 7M parameters, TRM obtains 45% test-accuracy on ARC-AGI-1 and 8% on ARC-AGI-2, higher than most LLMs (e.g., Deepseek R1, o3-mini, Gemini 2.5 Pro) with less than 0.01% of the parameters.

Mistral Small 3.2

Mistral Small is a 24B param LLM that

Running in Ollama

ollama pull hf.co/gabriellarson/Mistral-Small-3.2-24B-Instruct-2506-GGUF:Q4_K_M

References

Hierarchical Reasoning Model

Paper URL: https://arxiv.org/pdf/2506.21734 
Code Repo: https://github.com/sapientinc/HRM 

HRM is an alternative to transformer architecture that is better able to reason. It outperforms transformer-based LLMs at ARC-AGI2 with only 27M parameters.

Training a 27M Parameter Model with 1000 Examples

In the paper the authors refer to the fact that they only use between 1000 and 10,000 examples for specific problem domains:

This may seem quite low considering that this is a 27M parameter neural network and it seems likely that the network would be underfit after so few examples. The authors provide some additional clarifications around this point:

  1. Data augmentation is used in order to functionally boost the size of the training set.
  2. The authors use deep supervision to augment the training process (rather than relying on back-propagation alone).
  3. The problem domain is simpler than for language - particularly for things like Sudoku and ARC-AGI - these are structured grid type problems.

Gemma 4

Gemma 4

Released March 31, 2026 by Google DeepMind. Apache 2.0 licensed. Multimodal (text + image, audio on small models).

Model Sizes

Model Type Effective Params Context Modalities
E2B Dense 2.3B (5.1B w/ embeddings) 128K Text, Image, Audio
E4B Dense 4.5B (8B w/ embeddings) 128K Text, Image, Audio
26B A4B MoE 3.8B active / 25.2B total 256K Text, Image
31B Dense 30.7B 256K Text, Image

The 26B A4B is the standout — a MoE model that runs almost as fast as a 4B model despite 26B total params. The E2B/E4B use Per-Layer Embeddings for on-device efficiency.

Local Running Options

  1. Ollama — ollama run google/gemma-4 (all sizes). Easiest one-command setup.
  2. llama.cpp — GGUF quantized versions available on Hugging Face. Good for CPU/GPU hybrid inference.
  3. vLLM — For higher-throughput server deployment. Supports the native HF safetensor weights.
  4. LM Studio — GUI-based, supports GGUF formats. Good for desktop use.
  5. Hugging Face Transformers — Direct Python API. Full precision or QLoRA fine-tuning.

Hardware Requirements (rough)

The 26B A4B is generally considered the sweet spot for local use — frontier-level benchmarks (82.6 MMLU Pro, 88.3 AIME) with ~4B active parameter compute cost.

All models are on Hugging Face under google/gemma-4-*.