tinymixtral-v1.1-0.5b

tinymixtral-v1.1-0.5b — 0.5B-class MoE

Dense-ish small MoE (~432M total / ~176M active, top-2 of 6 routed experts) trained on the SmolLM-inspired blend — the "data quality" ablation that replaced C4 with FineWeb-Edu + Cosmopedia v2.

• HF: mikecovlee/tinymixtral-v1.1-0.5b

The repository name says "0.5b" (storage/coarse size class); the trained model is ~432M total parameters, of which ~176M are active per token.

Model details

Training

• Data: FineWeb-Edu + Cosmopedia v2 ≈ 89:11 (sampling weights 36:4), 4B tokens.
• LR: 7e-4 (swept over {1e-4, 3e-4, 5e-4, 7e-4} on 100M-token runs; 7e-4 best).
• Batch: 24 × 1024; cosine schedule with 2,000-step warmup.
• Tokenizer: TinyLlama (32k SentencePiece).

Evaluation

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

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 4B tokens for v1.1 (~1000× less) on a single consumer GPU — the gap is primarily a data-budget difference.

The C4 → SmolLM data-quality switch mainly improved ARC-Easy (+3.4pp) and BoolQ (+2.7pp) versus the legacy v1.0 C4 model (mean 0.403 → 0.409); ARC-Challenge was unchanged (0.247).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("mikecovlee/tinymixtral-v1.1-0.5b", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("mikecovlee/tinymixtral-v1.1-0.5b")

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

Limitations

• Base model only: no instruction tuning or safety alignment.
• Trained on only 4B tokens; English-centric. Superseded by v3.0.

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 — 1B MoE (earlier flagship)
• mikecovlee/tinymixtral-v1.1-0.5b — this model (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{tinymixtralv1.10.5b2026,
  title  = {TinyMixtral: a small Mixture-of-Experts language-model family},
  author = {Michael Lee},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/mikecovlee/tinymixtral-v1.1-0.5b}}
}

License

MIT (Copyright (C) 2026 Michael Lee).