Instructions to use nkthebass/tinybrainbot-3m-loop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nkthebass/tinybrainbot-3m-loop with Transformers:

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

pipe = pipeline("text-generation", model="nkthebass/tinybrainbot-3m-loop", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("nkthebass/tinybrainbot-3m-loop", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nkthebass/tinybrainbot-3m-loop with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nkthebass/tinybrainbot-3m-loop"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nkthebass/tinybrainbot-3m-loop",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nkthebass/tinybrainbot-3m-loop

SGLang

How to use nkthebass/tinybrainbot-3m-loop 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 "nkthebass/tinybrainbot-3m-loop" \
    --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": "nkthebass/tinybrainbot-3m-loop",
		"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 "nkthebass/tinybrainbot-3m-loop" \
        --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": "nkthebass/tinybrainbot-3m-loop",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nkthebass/tinybrainbot-3m-loop with Docker Model Runner:
```
docker model run hf.co/nkthebass/tinybrainbot-3m-loop
```

tinybrainbot-3m-loop / config.json

nkthebass

TinyBrainBot-3M-Loop: 2.96M recurrent-depth model, final checkpoint

3020d73 verified 2 days ago

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1.07 kB

{
  "architectures": [
    "TBBLoopForCausalLM"
  ],
  "model_type": "tbbloop",
  "auto_map": {
    "AutoConfig": "configuration_tbbloop.TBBLoopConfig",
    "AutoModelForCausalLM": "modeling_tbbloop.TBBLoopForCausalLM"
  },
  "native": {
    "vocab_size": 4104,
    "hidden_size": 256,
    "n_layers": 3,
    "n_heads": 4,
    "head_dim": 64,
    "n_kv_heads": 2,
    "ffn_size": 560,
    "context_length": 1024,
    "rope_theta": 10000.0,
    "rms_norm_eps": 1e-05,
    "tie_embeddings": true,
    "init_std": 0.02,
    "dropout": 0.0,
    "gradient_checkpointing": false,
    "loop_unique": 4,
    "n_loops": 3,
    "loop_train_min": 2,
    "loop_train_max": 4,
    "digit_place": true,
    "digit_lo": 24,
    "digit_hi": 33,
    "place_vocab": 16,
    "embed_rank": 96
  },
  "eval_loops": 0,
  "vocab_size": 4104,
  "hidden_size": 256,
  "num_hidden_layers": 4,
  "max_position_embeddings": 1024,
  "tie_word_embeddings": true,
  "bos_token_id": 0,
  "eos_token_id": 0,
  "torch_dtype": "float32",
  "use_cache": false
}

	{
	"architectures": [
	"TBBLoopForCausalLM"
	],
	"model_type": "tbbloop",
	"auto_map": {
	"AutoConfig": "configuration_tbbloop.TBBLoopConfig",
	"AutoModelForCausalLM": "modeling_tbbloop.TBBLoopForCausalLM"
	},
	"native": {
	"vocab_size": 4104,
	"hidden_size": 256,
	"n_layers": 3,
	"n_heads": 4,
	"head_dim": 64,
	"n_kv_heads": 2,
	"ffn_size": 560,
	"context_length": 1024,
	"rope_theta": 10000.0,
	"rms_norm_eps": 1e-05,
	"tie_embeddings": true,
	"init_std": 0.02,
	"dropout": 0.0,
	"gradient_checkpointing": false,
	"loop_unique": 4,
	"n_loops": 3,
	"loop_train_min": 2,
	"loop_train_max": 4,
	"digit_place": true,
	"digit_lo": 24,
	"digit_hi": 33,
	"place_vocab": 16,
	"embed_rank": 96
	},
	"eval_loops": 0,
	"vocab_size": 4104,
	"hidden_size": 256,
	"num_hidden_layers": 4,
	"max_position_embeddings": 1024,
	"tie_word_embeddings": true,
	"bos_token_id": 0,
	"eos_token_id": 0,
	"torch_dtype": "float32",
	"use_cache": false
	}