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 / config.json

mikecovlee

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	{
	"architectures": [
	"TinyMixtralForCausalLM"
	],
	"attention_dropout": 0.0,
	"dtype": "float32",
	"expert_intermediate_size": 2389,
	"head_dim": 64,
	"hidden_size": 896,
	"initializer_range": 0.02,
	"max_position_embeddings": 2048,
	"model_type": "tinymixtral",
	"num_attention_heads": 14,
	"num_experts_per_tok": 2,
	"num_hidden_layers": 10,
	"num_key_value_heads": 2,
	"num_local_experts": 6,
	"rms_norm_eps": 1e-06,
	"rope_theta": 1000000.0,
	"router_aux_loss_coef": 0.01,
	"router_jitter_noise": 0.01,
	"transformers_version": "4.57.3",
	"vocab_size": 32000,
	"auto_map": {
	"AutoConfig": "configuration_tinymixtral.TinyMixtralConfig",
	"AutoModelForCausalLM": "modeling_tinymixtral.TinyMixtralForCausalLM"
	}
	}