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

Libraries

How to use mikecovlee/tinymixtral with Transformers:

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mikecovlee/tinymixtral with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mikecovlee/tinymixtral

SGLang

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

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

TinyMixtral

A small Mixtral-style Mixture-of-Experts causal language model (~432M total, ~176M active parameters) for pretraining research on a single consumer GPU.

Model Architecture

Parameter	Value
hidden_size	896
num_layers	10
Attention	Grouped Query Attention (14 heads / 2 KV heads)
Head dim	64
RoPE theta	1,000,000
Norm	RMSNorm
Experts	6 (top-2 routing)
Expert FFN	SwiGLU, intermediate = 2389 (8/3 × hidden_size)
Vocab size	32,000
Max position	2,048
Total params	~432M
Active params	~176M

Hardware & Environment

GPU: NVIDIA RTX A5000 24GB
CPU: AMD Ryzen 7 5800X
RAM: 32GB

Training Details

Precision: bf16 (model, AdamW states, autocast forward/backward)
Optimizer: AdamW (β=0.9,0.95, wd=0.1), weight decay only on ≥2D parameters
LR schedule: Cosine decay with linear warmup (warmup_steps=2000)
Gradient clipping: 1.0
Batch: 22 × 1024 = 22,528 tokens/step
Activation checkpointing: enabled (required for 24GB VRAM)
Data: C4-en, pre-tokenized to .pt shards (100M tokens each), cycled round-robin

License

Downloads last month: 159

mikecovlee
/

tinymixtral

TinyMixtral

Model Architecture

Hardware & Environment

Training Details

License

Dataset used to train mikecovlee/tinymixtral