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
```

HenrySentinel commited on Jul 2, 2025

Commit

a43cd09

verified ·

1 Parent(s): 31f64a1

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +68 -0

README.md ADDED Viewed

	@@ -0,0 +1,68 @@

+---
+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