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 / README.md

mikecovlee

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metadata

license: mit
datasets:
  - allenai/c4
  - HuggingFaceFW/fineweb-edu
  - HuggingFaceTB/smollm-corpus
pipeline_tag: text-generation
library_name: transformers

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

Results

Training Summary

Phase	Data	Tokens	Steps	Time	Start Loss	End Loss
Pretrain	C4-en	4B	177,557	77.1 h	10.5	3.0
Post-train	FineWeb-Edu + Cosmopedia v2 (50:50)	1B	44,390	20.8 h	3.05	2.0

Post-training used learning rate 5e-5 with 300-step re-warmup, continuing from the pretrain checkpoint with AdamW momentum preserved.

GLUE (zero-shot)

Task	Metric	Pretrain (4B C4)	Post-train (5B total)
SST2	accuracy	0.470	0.554
MRPC	accuracy / f1	0.338 / 0.069	0.706 / 0.815
QQP	accuracy / f1	0.470 / 0.412	0.530 / 0.342
QNLI	accuracy	0.494	0.452
RTE	accuracy	0.520	0.484
CoLA	MCC	0.089	0.006
MNLI	accuracy	0.348	0.348
MNLI-mm	accuracy	0.368	0.368
Mean	—	0.403	0.483

ARC

Task	Pretrain (4B C4)	Post-train (5B total)
ARC-C 0-shot	0.220	0.233
ARC-C 5-shot	0.223	0.246
ARC-E 0-shot	0.311	0.342
ARC-E 5-shot	0.320	0.348

Zero-shot evaluation uses conditional log-likelihood scoring over answer spans. All evals run on a single GPU with --limit 500 --batch-size 16 --max-length 512. Pretrain and post-train evaluated under identical settings for fair comparison.

mikecovlee
/

tinymixtral