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

Libraries

How to use mikecovlee/tinymixtral-1B with Transformers:

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

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

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mikecovlee/tinymixtral-1B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mikecovlee/tinymixtral-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mikecovlee/tinymixtral-1B"
# 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-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mikecovlee/tinymixtral-1B

SGLang

How to use mikecovlee/tinymixtral-1B 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-1B" \
    --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-1B",
		"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-1B" \
        --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-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

TinyMixtral 1B — Post-Trained

A 1.18B-parameter Mixture-of-Experts language model (351M active), post-trained on 1B tokens of educational and web text.

Model Details

Property	Value
Architecture	Decoder-only Transformer with Sparse MoE
Total Parameters	1,182,172,160
Active Parameters	~351M
Hidden Size	1024
Layers	16
Experts	8 (top-2 routing)
Attention Heads	16 query / 4 key-value (GQA)
Head Dimension	64
Intermediate Size	2,816 (per expert)
Vocabulary	32,000
Context Length	2,048
Position Encoding	RoPE (theta=1e6)
Activation	SiLU
Norm	RMSNorm
Tied Embeddings	Yes

Training

Pre-training (4B tokens):

Data: FineWeb-Edu + Cosmopedia (89:11 blend)
Schedule: WSD (warmup 2,000 → stable → decay)
Peak LR: 7e-4
Batch: 16 × 1,024 = 16,384 tokens/step
Steps: 244,141
Duration: ~102.5 hours

Post-training (1B tokens):

Data: FineWeb-Edu + Cosmopedia continuation (second 1B slice)
Schedule: WSD (warmup 2,000 → stable → decay)
Peak LR: 2e-5
Batch: 16 × 1,024
Steps: 60,975
Duration: ~25.9 hours

Benchmark Results

Harness (0-shot)

Benchmark	Score
HellaSwag (acc_norm)	0.313
PIQA (acc)	0.609
Winogrande (acc)	0.505
ARC-Easy (acc_norm)	0.410
ARC-Challenge (acc_norm)	0.272
OpenBookQA (acc_norm)	0.290
BoolQ (acc)	0.528
LAMBADA (acc)	0.195

IFEval (instruction-following)

Model	inst_strict
1B post-train	0.2338
v1.1	0.2182

SAMSum (Dialogue Summarization)

Model	ROUGE-1	ROUGE-2	ROUGE-L
1B (0-shot)	9.83	0.50	7.85
1B (fine-tuned, 15ep)	28.82	8.55	24.08
T5-small (60M)	35.7	13.4	31.4

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "publish_posttrain/",
    trust_remote_code=True,
    torch_dtype="bfloat16",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("publish_posttrain/", legacy=False)

prompt = "The capital of France is"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Limitations

Data budget: Trained on only 4B tokens (pre-training) + 1B tokens (post-training). Comparable models use 100-1000× more data.
Reasoning: Limited multi-step reasoning and mathematical capability.
Hallucination: May generate plausible but incorrect facts.
Context: Effective context is shorter than the 2,048-token window.

License

MIT License. See LICENSE for details.

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Datasets used to train mikecovlee/tinymixtral-1B

Collection including mikecovlee/tinymixtral-1B

Tiny Mixtral

Collection

A small Mixtral-style Mixture-of-Experts causal language model for pretraining research on a single consumer GPU. • 4 items • Updated about 24 hours ago