初始化项目，由ModelHub XC社区提供模型

Model: Shamima/babylm-2026-multilingual-uniform-100M
Source: Original Platform

README.md

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+---
+license: mit
+language:
+- en
+- nl
+- zh
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- babylm
+- babylm-2026
+- multilingual
+- llama
+- pretrained-from-scratch
+---
+
+# BabyLM 2026 — MultiLingual track baseline (byte-premium-uniform)
+
+A 110M-param Llama-style decoder pre-trained from scratch on the BabyBabelLM
+trilingual corpus (English, Dutch, Chinese), under the BabyLM 2026
+MultiLingual track rules: **100M reference tokens, byte-premium adjusted**,
+≤10 epochs.
+
+This is the *baseline* zero-point of our ablation grid. Subsequent runs vary
+the mixture allocation (loss-weighted, simultaneous-bilingual, typological-bridge
+curriculum, register-controlled) on top of an identical scaffold. The matching
+ablation paper is in preparation.
+
+## Architecture
+
+- Llama (HF `LlamaForCausalLM`) — RoPE, RMSNorm, SwiGLU, no biases, tied embeddings
+- 12 layers · 768 hidden · 12 heads · 2048 FFN
+- 1024 sequence length
+- 110,119,680 parameters
+
+## Tokenizer
+
+Joint byte-level BPE, 32,768 vocab, trained on a balanced 50M-char sample from
+each of EN/NL/ZH. The same tokenizer is shared across all three languages (see
+the data card for why a joint tokenizer is required: ZH is 6.8% Latin script).
+
+## Training
+
+- **Data:** `BabyLM-community/babylm-eng` + `babylm-nld` + `babylm-zho`
+  (BabyBabelLM 2026 100M tier). Full corpora loaded in memory and shuffled
+  (the Hub layout is category-clustered; streaming with reasonable buffers
+  produces a biased sample).
+- **Mixture:** byte-premium-uniform — equal share of *reference tokens* per
+  language (1/3 each), achieved by deficit-driven selection, not uniform doc
+  sampling (mean doc sizes differ across languages).
+- **Optimizer:** AdamW (β₁=0.9, β₂=0.95, wd=0.1), lr 6e-4, cosine to 10%, 100-step warmup
+- **Compute:** 4× NVIDIA A10G (23 GB), bf16, DDP, micro-batch 16 × grad-accum 2 (eff. batch 128 sequences = 131k tokens/step)
+- **Tokens consumed at this checkpoint:** 100,000,000 byte-premium-adjusted reference tokens
+- **Per-language epochs at this checkpoint:** ≈1.0 each (within the BabyLM ≤10-epoch cap)
+
+## Revisions
+
+The `chck_{N}M` revisions match the BabyLM eval pipeline's fast-eval naming:
+
+```
+chck_1M, chck_2M, ..., chck_9M, chck_10M, chck_20M, ..., chck_90M, chck_100M
+```
+
+Use `revision=chck_NM` to load any milestone. The default (`main`) is `chck_100M`.
+
+## How to evaluate
+
+```bash
+git clone https://github.com/babylm-org/babylm-eval
+cd babylm-eval/multilingual
+bash scripts/zeroshot_model.sh --model_name Shamima/babylm-2026-multilingual-uniform-100M
+bash scripts/zeroshot_model_fast_all.sh --model_name Shamima/babylm-2026-multilingual-uniform-100M
+```
+
+## Citation
+
+```
+@misc{babylm-2026-uniform,
+  title  = {BabyLM 2026 MultiLingual baseline (byte-premium-uniform)},
+  author = {Hossain, Shamima},
+  year   = {2026},
+  url    = {https://huggingface.co/Shamima/babylm-2026-multilingual-uniform-100M}
+}
+```
+
+Companion repo with audit, scaffold, and ablation configs:
+https://github.com/silvererudite/bb-lm-challenge-sub