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

Libraries

How to use mikecovlee/tinymixtral-it with Transformers:

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

pipe = pipeline("text-generation", model="mikecovlee/tinymixtral-it", 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-it", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mikecovlee/tinymixtral-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mikecovlee/tinymixtral-it"
# 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-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mikecovlee/tinymixtral-it

SGLang

How to use mikecovlee/tinymixtral-it 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-it" \
    --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-it",
		"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-it" \
        --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-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mikecovlee/tinymixtral-it with Docker Model Runner:
```
docker model run hf.co/mikecovlee/tinymixtral-it
```

tinymixtral-it — instruction-tuned MoE

tinymixtral-it (a.k.a. v3.0-it) is the instruction-tuned version of the MoE flagship mikecovlee/tinymixtral: 477.5M total / 276.1M active parameters (top-2 of 4 experts), 2048 context, 32k SentencePiece vocabulary.

It is the MoE counterpart of the dense mikecovlee/tinymistral-276m-it: the exact same 3M-tier SFT recipe (same data, hyper-parameters and schedule) is applied to both the MoE and the dense base, so the two instruction-tuned models can be compared at the same active-parameter count and the same SFT budget.

Model details

Parameter	Value
hidden_size	1024
num_layers	16
Attention	Grouped Query Attention (16 heads / 4 KV heads)
Head dim	64
RoPE theta	1,000,000
Norm	RMSNorm + per-head QK-Norm (pre-RoPE)
Experts	4 routed (top-2), aux loss 1e-3
Expert FFN	SwiGLU, intermediate = 2048
Vocab size	32,000 (tied embeddings)
Max position	2,048
Total params	~477.5M
Active params	~276.1M

Training

Initialization: the public base mikecovlee/tinymixtral (v3.0).
Data: 2,168,835 deduplicated and eval-decontaminated English instruction conversations, blended from 10 public sources (Tulu3, OpenHermes, SlimOrca, OpenOrca, UltraChat, MetaMath, OrcaMath, OpenMathInstruct-2, SQuAD2, TriviaQA).
Recipe: 1 epoch, sequence packing to 1024 tokens, batch 24, bf16, AdamW, lr 2e-5 (cosine, 100-step warmup), weight decay 0.1, seed 42.

Evaluation

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

Harness = mean of the 7 primary metrics (hellaswag acc_norm, piqa acc, winogrande acc, arc_easy acc, arc_challenge acc_norm, openbookqa acc_norm, lambada acc).

Model	7-task harness	7-task all-acc	GSM8K strict / flex	IFEval prompt / inst
`tinymixtral-it` (MoE + 3M SFT)	0.3994	0.3691	0.0182 / 0.0205	0.1756 / 0.2782
`tinymixtral` (MoE base)	0.3992	—	0.0000 / 0.0159	—
`tinymistral-276m-it` (dense + 3M SFT)	0.3892	0.3631	0.0174 / 0.0205	0.1460 / 0.2602
`tinymistral-276m` (dense base)	0.3904	0.3890	0.0000 / 0.0136	—

Harness note. Means quoted here use the 7-task harness (excludes BoolQ); the base v1.0/v3.0 cards report an 8-task mean (includes BoolQ). The two are not directly comparable.

Per-task (tinymixtral-it): hellaswag 0.340, piqa 0.634, winogrande 0.528, arc_easy 0.448, arc_challenge 0.260, openbookqa 0.302, lambada 0.289 — plus BoolQ 0.426, reported separately (BoolQ is excluded from the 7-task mean above, to match the dense iso-active comparison).

Additional metric:

Metric	Score
Open-ended answer quality (LLM rubric, 4,955 held-out prompts, 0–100)	15.0 ± 0.3

Note: relative to the base model, instruction following and open-ended answer quality improve substantially while multiple-choice common-sense accuracy drops (BoolQ is the most sensitive task). Arithmetic remains far below practical use. The dense counterpart receives the same SFT recipe and lands close on the 7-task harness (0.3892 vs 0.3994) but is less steerable (IFEval 0.1460 / 0.2602).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "mikecovlee/tinymixtral-it"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, dtype="bfloat16", device_map="auto")

messages = [{"role": "user", "content": "What is 12% of 250?"}]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(ids, max_new_tokens=256, do_sample=False)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

Conversational use via the bundled chat template (tokenizer.apply_chat_template); greedy decoding works well at this scale.

Limitations

Absolute numbers are bounded by the 477.5M MoE total / 276.1M active scale and the 8.05B-token pretraining budget; arithmetic remains far below practical use.
English-centric, no safety alignment.
Multiple-choice common-sense accuracy drops slightly after SFT (BoolQ most sensitive).

Family

mikecovlee/tinymixtral — 477.5M MoE base (v3.0 flagship)
mikecovlee/tinymixtral-it — this model (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 — 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{tinymixtralit2026,
  title  = {TinyMixtral: a small Mixture-of-Experts language-model family},
  author = {Michael Lee},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/mikecovlee/tinymixtral-it}}
}

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