Instructions to use mikecovlee/tinymixtral-v1.1-0.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mikecovlee/tinymixtral-v1.1-0.5b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mikecovlee/tinymixtral-v1.1-0.5b", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mikecovlee/tinymixtral-v1.1-0.5b", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mikecovlee/tinymixtral-v1.1-0.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mikecovlee/tinymixtral-v1.1-0.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mikecovlee/tinymixtral-v1.1-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mikecovlee/tinymixtral-v1.1-0.5b

SGLang

How to use mikecovlee/tinymixtral-v1.1-0.5b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mikecovlee/tinymixtral-v1.1-0.5b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mikecovlee/tinymixtral-v1.1-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mikecovlee/tinymixtral-v1.1-0.5b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mikecovlee/tinymixtral-v1.1-0.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mikecovlee/tinymixtral-v1.1-0.5b with Docker Model Runner:
```
docker model run hf.co/mikecovlee/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

Parameter	Value
Type	Sparse MoE, 6 routed experts (top-2)
Total params	~432M
Active params	~176M
Hidden size	896
Layers	10
Attention	GQA 14 Q / 2 KV heads (head dim 64)
Vocab	32,000
Context	2,048

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).

Task	Metric	v1.1 (432M)
HellaSwag	acc_norm	0.308
PIQA	acc	0.616
WinoGrande	acc	0.524
ARC-Easy	acc	0.456
ARC-Challenge	acc_norm	0.247
OpenBookQA	acc_norm	0.288
BoolQ	acc	0.606
LAMBADA	acc	0.227
Mean	—	0.409

Comparison with similar models

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

Task	Metric	v1.1 (432M)	SmolLM2-360M	Qwen3-0.6B
HellaSwag	acc_norm	0.308	0.563	0.473
PIQA	acc	0.616	0.719	0.673
WinoGrande	acc	0.524	0.587	0.563
ARC-Easy	acc	0.456	0.705	0.609
ARC-Challenge	acc_norm	0.247	0.383	0.340
OpenBookQA	acc_norm	0.288	0.372	0.316
BoolQ	acc	0.606	0.620	0.643
LAMBADA	acc	0.227	0.532	0.401

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

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Datasets used to train mikecovlee/tinymixtral-v1.1-0.5b

Collection including mikecovlee/tinymixtral-v1.1-0.5b

Tiny Mixtral

Collection

A small Mixtral-style Mixture-of-Experts causal language model for pretraining research on a single consumer GPU. • 9 items • Updated 21 days ago • 1