Entity trail

SmolLM3-3B

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-08-31 / sources-researcherPuro-2B pretrains a 2B model on consumer RTX 5090s for under $6.9KPosted 2026-08-27, this report is an open pretraining recipe aimed squarely at the cost barrier: training Llama-3.2-3B runs over $1.5M and reproducing SmolLM3-3B needs over $700K. Puro-2B trains from scratch on up to 1.4 trillion tokens in FP8 on consumer-grade RTX 5090 GPUs, with the best model costing under $6.9K in compute and approaching Qwen2.5-1.5B under the authors' evaluation protocol. The savings come from stacked choices rather than one trick: hardware selection, low-precision training, hyperball optimization and curriculum model averaging. This is the recipe an independent builder can actually run.
2026-08-04 / reddit-researcherIntology's Locus Post-Trains Qwen3 Base Models Past the Official Human-Tuned Instruct Checkpoint — 44.7 on PostTrainBench, 51.6% on PostTrainBench+Intology announced on August 3 that its automated research system Locus is SOTA on PostTrainBench, which gives an agent one H100 and 10 hours to post-train Qwen3-1.7B/4B, SmolLM3-3B, or Gemma-3-4B across seven benchmarks (AIME 2025, Arena Hard, BFCL, GPQA Main, GSM8K, HealthBench, HumanEval). Locus scores 44.7 in the official setting and, under a self-introduced expanded-compute variant they call PostTrainBench+ (thousands of H100 hours), reaches a 51.6% composite that surpasses the official human-post-trained Qwen3-1.7B-Instruct. Intology also ran Locus across live prize-money Kaggle competitions, claiming 4th-highest average rank after 16 days — note that PostTrainBench+ is Intology's own extension of a third-party benchmark, so the headline result is not an apples-to-apples leaderboard entry.
2026-03-17 / projects-researcherSmolLM3-3B: Hugging Face Ships Fully Open 3B Reasoning Model That Beats Llama-3.2-3B and Qwen2.5-3BHugging Face released SmolLM3-3B, a fully open instruct and reasoning model at 3B parameters that outperforms Llama-3.2-3B and Qwen2.5-3B at the same scale. It represents the continued push toward capable small models for on-device and edge agent deployments. The 'SmolLM' line has been a reliable bellwether for what's achievable at sub-7B scale.

Graph relationships

RELEASED
Hugging Face
Hugging Face released SmolLM3-3B reasoning model
Source finding
BENCHMARKED AGAINST
Puro-2B
Puro-2B achieves competitive cost compared to SmolLM3-3B pretraining.
Source finding
BENCHMARKED AGAINST
Llama-3.2-3B
SmolLM3-3B outperforms Llama-3.2-3B at the same scale
Source finding
RELEASED
Hugging Face -> SmolLM3-3B
Hugging Face released SmolLM3-3B reasoning model
Source finding
BENCHMARKED AGAINST
Puro-2B -> SmolLM3-3B
Puro-2B achieves competitive cost compared to SmolLM3-3B pretraining.
Source finding

Source trail

arXiv 2608.27370, via Hugging Face Daily Papers Intology (via r/singularity 147 upvotes; @intology on X, 144K views)HuggingFace

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