Instructions to use RetentionLabs/TTTPilot-Q-5B-Thinking-MAC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RetentionLabs/TTTPilot-Q-5B-Thinking-MAC with Transformers:

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

pipe = pipeline("text-generation", model="RetentionLabs/TTTPilot-Q-5B-Thinking-MAC", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("RetentionLabs/TTTPilot-Q-5B-Thinking-MAC", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RetentionLabs/TTTPilot-Q-5B-Thinking-MAC with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RetentionLabs/TTTPilot-Q-5B-Thinking-MAC"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RetentionLabs/TTTPilot-Q-5B-Thinking-MAC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RetentionLabs/TTTPilot-Q-5B-Thinking-MAC

SGLang

How to use RetentionLabs/TTTPilot-Q-5B-Thinking-MAC 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 "RetentionLabs/TTTPilot-Q-5B-Thinking-MAC" \
    --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": "RetentionLabs/TTTPilot-Q-5B-Thinking-MAC",
		"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 "RetentionLabs/TTTPilot-Q-5B-Thinking-MAC" \
        --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": "RetentionLabs/TTTPilot-Q-5B-Thinking-MAC",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RetentionLabs/TTTPilot-Q-5B-Thinking-MAC with Docker Model Runner:
```
docker model run hf.co/RetentionLabs/TTTPilot-Q-5B-Thinking-MAC
```

TTTPilot-Q-5B-Thinking-MAC

File size: 1,240 Bytes

{
  "model_type": "tttpilot_mac",
  "architectures": [
    "TTTPilotMACForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "modeling_tttpilot_mac.TTTPilotMACConfig",
    "AutoModel": "modeling_tttpilot_mac.TTTPilotMACModel",
    "AutoModelForCausalLM": "modeling_tttpilot_mac.TTTPilotMACForCausalLM"
  },
  "vocab_size": 151936,
  "bos_token_id": 151643,
  "eos_token_id": 151645,
  "pad_token_id": 151643,
  "tie_word_embeddings": true,
  "hidden_size": 2048,
  "hidden_act": "silu",
  "initializer_range": 0.02,
  "rms_norm_eps": 1e-06,
  "max_position_embeddings": 262144,
  "num_memory_layers": 24,
  "memory_intermediate_size": 5504,
  "num_attention_heads": 32,
  "mini_batch_size": 16,
  "ttt_base_lr": 1.0,
  "ttt_layer_type": "linear",
  "pre_conv": true,
  "conv_kernel": 4,
  "use_gate": true,
  "share_qk": true,
  "fixed_memory_size": 64,
  "num_core_layers": 36,
  "core_intermediate_size": 9728,
  "num_key_value_heads": 8,
  "head_dim": 128,
  "attention_bias": false,
  "attention_dropout": 0.0,
  "rope_theta": 5000000,
  "rope_scaling": null,
  "sliding_window": null,
  "use_cache": true,
  "scan_checkpoint_group_size": 0,
  "torch_dtype": "bfloat16",
  "pretraining_tp": 1
}