AliceYin/l20-edu-135m

L20 Edu 135M Stage 4

l20-edu-135m is a 134.5M-parameter Llama-style language model trained and improved on a single NVIDIA L20 GPU. The default checkpoint is the selected Stage 4 anti-forgetting release: an 87.5% SFT checkpoint blended with 12.5% Stage 4 base weights, chosen by six-task regression gates.

The key result is token and compute efficiency: the released model uses roughly 13B pretraining/continual-pretraining tokens total (10B initial FineWeb-Edu pretraining + 3B Stage 4 curated continuation), while public 135M SmolLM references use far larger budgets: SmolLM-135M reports 600B pretraining tokens and SmolLM2-135M reports 2T pretraining tokens. That puts this release at about 2.2% of SmolLM-135M token budget and about 0.65% of SmolLM2-135M token budget, trained on one L20 rather than the 64 H100 setup reported by the SmolLM model cards.

Highlights

• —Parameters: 134,515,008
• —Hardware: single NVIDIA L20 GPU
• —Initial pretraining: 10,001,252,352 FineWeb-Edu tokens
• —Stage 4 continuation: 3,000,000,965 curated tokens
• —Released pretraining-token total: about 13.0B tokens
• —Throughput from the initial 10B run: 38.5k tokens/s mean after warmup
• —Context length: 8,192 tokens for Stage 4 continued pretraining
• —Data filtering: cross-source MinHash/LSH deduplication, sentence/paragraph deduplication, benchmark decontamination, and LCS overlap removal
• —Selection: benchmark regression gates and base/SFT interpolation to preserve general benchmark performance

Token-Budget Context

This comparison is about training-budget context, not a claim of identical data, tokenizer, architecture, or benchmark protocol. The useful takeaway is that the project demonstrates a complete small-model pretraining, curation, evaluation, SFT, and release pipeline under a much smaller single-GPU budget.

Benchmark Comparison

These are same-protocol self-run numbers on the released checkpoints. The key same-size comparison is SmolLM-135M: this release is 0.0617 mean points behind while using about 2.2% of SmolLM-135M's reported token budget and a single L20 instead of the 64 H100 setup reported in its model card. Qwen2.5-0.5B and OLMo-1B are included as larger reference/upper-bound checkpoints, not same-size baselines.

Detailed task-level scores are included in eval_results/stage4_release/model_comparison/summary.md, summary.csv, and summary.json.

Selected Six-Task Results

Regression gate: passed. The selected SFT/interpolated release reaches a six-task mean of 0.4150 versus 0.4141 for the Stage 4 base.

Stage 4 Base Results

Data Gate

• —Status: pass
• —Validation tokens: 4,194,398
• —Stage 4 indexed documents: 3,312,229
• —Indexed sentence/paragraph segments: 34,852,069
• —Benchmark-contaminated documents removed: 23
• —Benchmark audit: ARC-Challenge, ARC-Easy, HellaSwag, PIQA, LAMBADA OpenAI, and WinoGrande
• —Matching: 13-gram candidates plus token LCS overlap >= 0.60 removal
• —Deduplication: 64-permutation MinHash with LSH candidate search across sources

Training And Selection

• —Initial model: 10B-token from-scratch FineWeb-Edu run on one L20
• —Stage 4 data: high-quality cross-deduplicated educational/code/reasoning mix
• —Stage 4 selected base checkpoint: step 2500
• —Selected validation loss: 2.9263725876808167
• —SFT data: filtered HuggingFaceTB/smol-smoltalk style data for the 135M model
• —SFT training rows: 426,842
• —Anti-forgetting: model soup/interpolation candidates selected by six-task regression gates

Reproducibility

Evaluation uses lm-evaluation-harness with fixed seed and full zero-shot task datasets for ARC-Challenge, ARC-Easy, HellaSwag, LAMBADA OpenAI, PIQA, and WinoGrande. Artifacts and summaries are included under eval_results/ in the model repository.

Generated: 2026-06-16T16:55:19.734757+00:00

Intended Use

This is a research model for small-model pretraining, data curation, continual pretraining, evaluation, and downstream fine-tuning experiments. Users should independently validate factuality, safety, and task suitability before deployment.