Instructions to use ml-ryanlee/looped-16x2-32L-d640-1e18-a100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ml-ryanlee/looped-16x2-32L-d640-1e18-a100 with Transformers:

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

pipe = pipeline("text-generation", model="ml-ryanlee/looped-16x2-32L-d640-1e18-a100", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ml-ryanlee/looped-16x2-32L-d640-1e18-a100", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ml-ryanlee/looped-16x2-32L-d640-1e18-a100 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ml-ryanlee/looped-16x2-32L-d640-1e18-a100"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ml-ryanlee/looped-16x2-32L-d640-1e18-a100",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ml-ryanlee/looped-16x2-32L-d640-1e18-a100

SGLang

How to use ml-ryanlee/looped-16x2-32L-d640-1e18-a100 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 "ml-ryanlee/looped-16x2-32L-d640-1e18-a100" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ml-ryanlee/looped-16x2-32L-d640-1e18-a100",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ml-ryanlee/looped-16x2-32L-d640-1e18-a100" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ml-ryanlee/looped-16x2-32L-d640-1e18-a100",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ml-ryanlee/looped-16x2-32L-d640-1e18-a100 with Docker Model Runner:
```
docker model run hf.co/ml-ryanlee/looped-16x2-32L-d640-1e18-a100
```

looped-16x2-32L-d640-1e18-a100 / train_config.json

ml-ryanlee

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	{
	"active_params": 222926080,
	"amp_dtype": "torch.bfloat16",
	"arch": "looped",
	"base_d_ff": 704,
	"base_d_model": 256,
	"batch_size": 16,
	"beta1": 0.9,
	"beta2": 0.999,
	"context_length": 1024,
	"d_ff": 1728,
	"d_ff_run": 1728,
	"d_model": 640,
	"d_model_run": 640,
	"effective_depth": 32,
	"eps": 1e-08,
	"estimated_flops_active": 1000003621607178240,
	"eval_batches": 50,
	"flop_budget": 1e+18,
	"init_seed": 42,
	"looped": true,
	"lr": 0.005,
	"moe": false,
	"mup": true,
	"num_heads": 10,
	"num_heads_run": 10,
	"num_layers_in_stack": 16,
	"num_params": 143627520,
	"num_stacks": 2,
	"rope_theta": 10000.0,
	"steps": 45632,
	"steps_run": 45632,
	"train_path": "data/fw_train_tokens.npy",
	"val_path": "data/fw_valid_tokens.npy",
	"vocab_size": 50257,
	"wandb_project": "looped-scaling-32L-isoflop",
	"warmup": 4563,
	"warmup_frac": 0.1,
	"weight_decay": 0.0001,
	"weight_tied": false,
	"width_ratio": 2.5
	}