slm
Datasets
All datasets matching “slm”chess-slm-benchmarkbenchmark
SLM Lab
Modular Deep Reinforcement Learning framework in PyTorch.
Companion library of the book Foundations of Deep Reinforcement Learning.
Documentation · Benchmark Results
NOTE: v5.0 updates to Gymnasium, uv tooling, and modern dependencies with ARM support - see CHANGELOG.md.
Book readers: git checkout v4.1.1 for Foundations of Deep Reinforcement Learning code.
BeamRider
Breakout
KungFuMaster
MsPacman
Pong
Qbert
Seaquest
Sp.Invaders… See the full description on the dataset page: https://huggingface.co/datasets/SLM-Lab/benchmark.slm-parameter-audit
SLM card-vs-artifact parameter audit
An autonomous audit of small-language-model repos on the Hugging Face Hub. For each
in-scope model (independent builders training very small models from scratch, roughly
0.5M–500M parameters), the parameter count stated in the model card is compared against
the actual artifact: the safetensors header, config.json, and the training script where
present. A mismatch is recorded when the card's number does not match the artifact's
real parameter… See the full description on the dataset page: https://huggingface.co/datasets/Compactbot/slm-parameter-audit.slm-388m-adjaxtSFTset-SLM
SFTset-SLM
Source-aware shuffled supervised fine-tuning data formatted for LiquidAI/LFM2.5-1.2B-Instruct.
Dataset summary
Conversations: 3,091,614
Tokens: 1,670,601,639
Parquet parts: 11
Target Parquet file size: 500 MiB
Tokenizer: LiquidAI/LFM2.5-1.2B-Instruct
Shuffle seed: 1337
token_count includes ChatML turn-end tokens; no extra terminal EOS is appended.
Columns
chatml: LFM2.5 template text starting with <|im_start|> and containing ChatML… See the full description on the dataset page: https://huggingface.co/datasets/AxiomicLabs/SFTset-SLM.slm-lab-data
slm-lab-data
Everything slm-lab produced that is not a model: the synthetic task it
generated, the corpora it packed, the tokenizers it trained from scratch, and
every published result file.
What it is for. Two things a reader can actually do with it. The browsable
configs below are training data with ground truth that is correct by
construction — the expense→JSON task is generated by
scripts/gen_json_task.py, so every target is exact, including the computed
dates. And results/… See the full description on the dataset page: https://huggingface.co/datasets/Dhevenddra/slm-lab-data.