--- license: mit language: - en pretty_name: slm-lab data, tokenizers and results tags: - educational - tokenizers - synthetic configs: - config_name: expense_to_json default: true data_files: - split: train path: json_train.jsonl - split: validation path: json_val.jsonl - split: test path: json_test.jsonl - config_name: expense_to_json_preference_prompts data_files: - split: train path: json_pref.jsonl - config_name: preference_pairs data_files: - split: train path: results/phase07_pairs.jsonl - config_name: chat_sft data_files: - split: train path: sft_v2_train.jsonl - split: validation path: sft_v2_val.jsonl - config_name: chat_sft_v1 data_files: - split: train path: sft_train.jsonl - split: validation path: sft_val.jsonl - config_name: scored_generations data_files: - split: train path: results/phase05_scores.jsonl --- # 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/` holds every measurement this project made, including the negative ones, in the form they were published. ## Browse it Six configs are set up for the dataset viewer. `expense_to_json` is the default and the place to start. | config | rows are | what it was used for | |---|---|---| | **`expense_to_json`** | a messy expense line, a reference date, the exact JSON target, and the chat messages built from them | the fine-tuning task: train / validation / test | | `expense_to_json_preference_prompts` | the same shape, on held-out lines | the prompts the preference pairs were generated from | | `preference_pairs` | prompt, chosen, rejected, plus which fields the rejected one got wrong | DPO training | | `chat_sft` | chat `messages` | instruction tuning the 12.6M model | | `chat_sft_v1` | chat `messages` | the superseded first attempt, kept because the pair of runs is the result | | `scored_generations` | model, prompt, generated text, hand-scored responsiveness | the evaluation-by-reading pass | The rest of the repo is files rather than tables, and the viewer is told to leave them alone: | path | what it is | |---|---| | `tinystories_.json` | byte-level BPE tokenizers trained from scratch at vocab 2048 / 8192 / 32768 | | `tinystories__{train,val}.bin` | TinyStories packed to `uint16` token streams | | `chat_8192*` | the chat-formatted corpus for mid-training, packed | | `sweeps/` | one `metrics.json` per ablation arm | | `results/` | **every published write-up, table, figure and score file** | ## The results are the point `results/` holds the project's measured findings in full, including the ones that went the wrong way: - `summary.md` — 24 runs, 7 ablations, judged against a **measured seed noise floor of 0.0146 nats**. Two of seven showed no detectable difference and are reported as such. - `ablation_vocab_size.md` — and the note explaining that correcting the bits-per-byte metric **reversed this ablation's verdict**, because the original was per-token loss times a shared constant and so preserved the exact artifact it existed to remove. - `phase08_compare.md` — every production quantization method on one table, including the KL column almost nobody publishes. - `phase08_granularity.md` — quantization granularity measured by hand on one real weight matrix, with the outlier experiment. - `phase09_scaling.md` — the capability floor, bracketed across a 98× parameter range, with the chance baseline next to every score. ## A note on the tokenizers Three vocabularies were trained so the choice could be measured rather than assumed. The 32768 arm wins on bits-per-byte — but its embedding table is 12.6M parameters, which at this project's budget is the *entire* model, leaving 17K parameters for the layers. The measured winner and the usable choice are not the same thing, and the results say so. ## Limitations Specific, and written out in full — this is the section a knowledgeable reader checks first. - **Everything here is synthetic.** TinyStories, a public chat corpus, and a task generated by this project's own script. There is no personal data in any of it, and no human-written text that was not already public. - **The task is narrow by design.** One schema, seven fields, one domain. It was chosen because its ground truth can be known exactly, not because it is representative. - **The generated corpus inherits its generator's blind spots.** The merchant names, phrasings and date expressions come from fixed vocabularies in `gen_json_task.py`, so a model that fits this task has not been shown the variety real expense text has. - **Models trained on it are research artifacts for learning, not products.** ## Where this came from Part of **slm-lab** — a language model built end to end on free compute: a hand-written BPE tokenizer, a transformer from scratch, a controlled ablation study with a measured noise floor, post-training, QLoRA on a 1B model, DPO, and quantization written by hand and benchmarked against every production method. - **Start here:** the [SLM Lab collection](https://huggingface.co/collections/Dhevenddra/slm-lab) lists every repo in the order it was built, with a one-line finding on each. - **Code, guide and the full failure log:** https://github.com/Dhevenddra/slm-lab - **Every result file, dataset and tokenizer:** This repo is the project's **data, tokenizers and published results** stage; the published write-up for it is under `results/` in the dataset repo, named for that phase. The project's own failure log (`FAILURES.md`, 26 entries) is the part most worth reading. The recurring shape: *the instrument measured something correlated with the question rather than equal to it.*