tinymixtral-v1.1-1b

tinymixtral-v1.1-1b — 1B MoE

Earlier flagship (before v3.0). A wider sparse MoE: ~1.18B total / ~352M active params (top-2 of 8 routed experts), trained on a SmolLM-inspired blend.

• HF: mikecovlee/tinymixtral-v1.1-1b

Model details

Training

* Decay-phase loss plateaued at ~1.9 then spiked to 3.4 in the final ~300 steps as the data stream wrapped back to the start of the corpus.

Data: FineWeb-Edu + Cosmopedia v2 (89:11). Pretrain batch 16 × 1024 (16,384 tokens/step) → 4B tokens in 244,141 steps; warmup 2,000 steps. The continuation added 4B tokens in 121,921 steps (~32k tokens/step).

Evaluation

Measured with lm-evaluation-harness v0.4.12, 0-shot (cuda, bf16).

Doubling pretrain tokens improved every task (BoolQ +7.2pp, LAMBADA +3.5pp, mean +2.3pp).

Few-shot: HellaSwag 10-shot acc_norm 0.312 · WinoGrande 5-shot acc 0.524 · ARC-Easy 25-shot acc_norm 0.468 · ARC-Challenge 25-shot acc_norm 0.262.

Comparison with similar models

Same suite and settings, measured locally (lm-evaluation-harness v0.4.12, 0-shot, cuda, bf16):

SmolLM2-360M was trained on 4T tokens and Qwen3-0.6B on 36T tokens, versus 8B tokens for the 1B MoE on a single consumer GPU; the gap is primarily a data-budget difference.

Instruction following (IFEval)

IFEval (instruction-level loose accuracy), --apply_chat_template:

Instruction-following stays flat (~0.22) — more pretraining tokens do not improve it.

Post-training (negative result)

Post-trained from the lowest-loss pretrain checkpoint with the v1.1 recipe (Wiki + Cosmopedia v2 50:50, 1B tokens, lr 2e-5, warmup 300, WSD, 60,975 steps, ~26 h). Loss fell 2.98 → 1.8 but downstream metrics were unchanged:

At this scale, low-LR post-training on a knowledge blend lowers the loss but does not transfer to task ability.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("mikecovlee/tinymixtral-v1.1-1b", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("mikecovlee/tinymixtral-v1.1-1b")

Requires transformers and trust_remote_code=True (custom tinymixtral architecture).

Limitations

• Base model only: no instruction tuning or safety alignment.
• Trained on 8B tokens; English-centric. Superseded by the v3.0 flagship.

Family

• mikecovlee/tinymixtral — 477.5M MoE base (v3.0 flagship)
• mikecovlee/tinymixtral-it — MoE instruction-tuned (v3.0-it, 3M SFT)
• mikecovlee/tinymistral-276m — 276M dense base (iso-active ablation)
• mikecovlee/tinymistral-276m-it — 276M dense instruction-tuned (3M SFT)
• mikecovlee/tinymixtral-v1.1-1b — this model (1B MoE, earlier flagship)
• mikecovlee/tinymixtral-v1.1-0.5b — 0.5B-class MoE (data-quality ablation)
• mikecovlee/tinymixtral-v2.0-beta — shared-expert experiment (beta)
• mikecovlee/tinymixtral-v1.0 — legacy (C4)

Naming. The MoE family is published under tinymixtral; the dense 276M iso-active ablation companions use the tinymistral spelling. Both belong to the same project.

Citation

@misc{tinymixtralv1.11b2026,
  title  = {TinyMixtral: a small Mixture-of-Experts language-model family},
  author = {Michael Lee},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/mikecovlee/tinymixtral-v1.1-1b}}
}

License

MIT (Copyright (C) 2026 Michael Lee).