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 / training_metadata.json

GGUFGuy

AppleMind 1.0 Mini 300M-token pretraining complete

76c06d3 verified about 24 hours ago

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884 Bytes

	{
	"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"
	}
	}