Instructions to use AppleMind-AI/AppleMind-1.0-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AppleMind-AI/AppleMind-1.0-Mini with Transformers:

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

pipe = pipeline("text-generation", model="AppleMind-AI/AppleMind-1.0-Mini")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AppleMind-AI/AppleMind-1.0-Mini")
model = AutoModelForCausalLM.from_pretrained("AppleMind-AI/AppleMind-1.0-Mini", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AppleMind-AI/AppleMind-1.0-Mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AppleMind-AI/AppleMind-1.0-Mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AppleMind-AI/AppleMind-1.0-Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AppleMind-AI/AppleMind-1.0-Mini

SGLang

How to use AppleMind-AI/AppleMind-1.0-Mini 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 "AppleMind-AI/AppleMind-1.0-Mini" \
    --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": "AppleMind-AI/AppleMind-1.0-Mini",
		"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 "AppleMind-AI/AppleMind-1.0-Mini" \
        --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": "AppleMind-AI/AppleMind-1.0-Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AppleMind-AI/AppleMind-1.0-Mini with Docker Model Runner:
```
docker model run hf.co/AppleMind-AI/AppleMind-1.0-Mini
```

AppleMind-1.0-Mini

File size: 884 Bytes

76c06d3

{
  "model": "AppleMind 1.0 Mini",
  "parameters": 1020480,
  "trainable_parameters": 1020480,
  "final_steps": 2288,
  "final_tokens": 299892736,
  "target_tokens": 300000000,
  "actual_tokens_per_parameter": 293.8741925368454,
  "dataset": {
    "fineweb_edu": 100000000,
    "fineweb_hq": 100000000,
    "smollm": 100000000
  },
  "model_config": {
    "n_layer": 2,
    "n_head": 2,
    "n_embd": 20,
    "context_length": 256,
    "vocab_size": 50260
  },
  "training": {
    "batch_size": 64,
    "gradient_accumulation": 8,
    "tokens_per_micro_batch": 16384,
    "tokens_per_step": 131072,
    "learning_rate": 3e-05,
    "weight_decay": 0.1,
    "gradient_clip": 1.0,
    "bf16": true,
    "tf32": true,
    "torch_compile": false
  },
  "repositories": {
    "checkpoints": "AppleMind-AI/AppleMind-1.0-Mini-Checkpoints",
    "final": "AppleMind-AI/AppleMind-1.0-Mini"
  }
}