Instructions to use mikecovlee/tinymistral-276m-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mikecovlee/tinymistral-276m-it with Transformers:

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

pipe = pipeline("text-generation", model="mikecovlee/tinymistral-276m-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/tinymistral-276m-it", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mikecovlee/tinymistral-276m-it with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mikecovlee/tinymistral-276m-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/tinymistral-276m-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mikecovlee/tinymistral-276m-it

SGLang

How to use mikecovlee/tinymistral-276m-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/tinymistral-276m-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/tinymistral-276m-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/tinymistral-276m-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/tinymistral-276m-it",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mikecovlee/tinymistral-276m-it with Docker Model Runner:
```
docker model run hf.co/mikecovlee/tinymistral-276m-it
```

tinymistral-276m-it — instruction-tuned dense

tinymistral-276m-it is the instruction-tuned version of the 276M dense base mikecovlee/tinymistral-276m. It is the dense counterpart of the MoE flagship mikecovlee/tinymixtral-it: the exact same 3M-tier SFT recipe (same data, hyper-parameters and schedule) is applied to 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

	`tinymistral-276m-it`	`tinymixtral-it` (MoE)
Architecture	Dense SwiGLU FFN	4 routed experts, top-2, aux 1e-3
Total parameters	276,073,472	477,465,600
Active parameters	276,073,472	276,139,008
FFN intermediate	4096	2048 (per expert)
Hidden size	1024	1024
Layers	16	16
Attention	GQA 16 Q / 4 KV heads, head_dim 64	same
Context length	2048	2048
Positional	RoPE θ = 1e6, QK-Norm	same
Norm / embeddings	Pre-RMSNorm (eps 1e-6), tied embeddings	same
Vocab	32,000 (TinyLlama tokenizer)	same
Precision	float32 checkpoint (bf16 training)	same
License	MIT	MIT

Training

Initialization: the 8.05B-token final checkpoint of the dense base tinymistral-276m.
SFT recipe: identical to the MoE flagship tinymixtral-it (3M tier) — 2,168,835 deduplicated, eval-decontaminated English instruction conversations blended from 10 public sources (Tulu3, OpenHermes, SlimOrca, OpenOrca, UltraChat, MetaMath, OrcaMath, OpenMathInstruct-2, SQuAD2, TriviaQA), 1 epoch, sequence packing to 1024 tokens, batch 24, AdamW lr 2e-5 (cosine, 100-step warmup), weight decay 0.1, seed 42 → 56,793 steps.
Hardware: single NVIDIA RTX PRO 4500 (Blackwell, 32 GB), 1036.5 min (17.3 h).

Evaluation

Same-hardware evaluation (lm_eval 0.4.12, 0-shot, no chat template). 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` (dense base)	0.3904	0.3890	0.0000 / 0.0136	—
`tinymistral-276m-it`	0.3892	0.3631	0.0174 / 0.0205	0.1460 / 0.2602

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.

Takeaways:

SFT transfers to the dense base: GSM8K flexible rises from 0.0136 (dense base) to 0.0205, matching the MoE flagship under the same recipe.
The 7-task harness barely moves (0.3904 → 0.3892), the same pattern as the MoE flagship — SFT mainly buys instruction-following and math, not general tasks.
Instruction following is weaker on the dense base: IFEval 0.1460 / 0.2602 vs 0.1756 / 0.2782 for tinymixtral-it — the dense base is less steerable than the MoE base.
Absolute numbers remain bounded by the 276M scale and the 8.05B-token pretraining budget.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "mikecovlee/tinymistral-276m-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))

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

Limitations

The base was pretrained on only 8.05B tokens — far below modern small-model budgets; knowledge tasks (MMLU / TruthfulQA) are near chance.
Math/reasoning is bounded by the 276M scale and the pretraining budget; SFT only helps marginally.
English-centric, no safety alignment.

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 — this model (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{tinymistral276mit2026,
  title  = {TinyMixtral: a small Mixture-of-Experts language-model family},
  author = {Michael Lee},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/mikecovlee/tinymistral-276m-it}}
}

License

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