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, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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 / README.md

HenrySentinel

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	---
	language: en
	license: apache-2.0
	library_name: transformers
	tags:
	- text-generation
	- pytorch
	- gpt
	- language-model
	---

	# 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

	```python
	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