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

Libraries

How to use HenrySentinel/tinyMind with Transformers:

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

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

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("HenrySentinel/tinyMind", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HenrySentinel/tinyMind with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HenrySentinel/tinyMind"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HenrySentinel/tinyMind",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HenrySentinel/tinyMind

SGLang

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

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

tinyMind

This is a small transformer language model trained from scratch with approximately 17,731,328 parameters.

Model Details

Architecture: GPT-style transformer
Parameters: ~17M
Layers: 6
Attention Heads: 8
Embedding Dimension: 256
Max Sequence Length: 512
Vocabulary Size: 50257

Training Data

The model was trained on a diverse mixture of high-quality text data including:

OpenWebText
Wikipedia articles
BookCorpus
Other curated text sources

Usage

from transformers import GPT2TokenizerFast, AutoModelForCausalLM

tokenizer = GPT2TokenizerFast.from_pretrained("HenrySentinel/tinyMind")
model = AutoModelForCausalLM.from_pretrained("HenrySentinel/tinyMind")

# Generate text
input_text = "The key to artificial intelligence is"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids, max_length=100, temperature=0.8, do_sample=True)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)

Training Details

Optimizer: AdamW with cosine learning rate scheduling
Learning Rate: 0.001
Batch Size: 8
Sequence Length: 512
Epochs: 3
Gradient Clipping: 1.0

Limitations

This is a small model designed for experimentation and learning. It may:

Generate inconsistent or factually incorrect content
Have limited knowledge compared to larger models
Require careful prompt engineering for best results

License

Apache 2.0

Downloads last month: 1,695

Safetensors

Model size

17.7M params

Tensor type

F32