Instructions to use hf-internal-testing/tiny-random-MambaForCausalLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hf-internal-testing/tiny-random-MambaForCausalLM with Transformers:

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

pipe = pipeline("text-generation", model="hf-internal-testing/tiny-random-MambaForCausalLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MambaForCausalLM")
model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-MambaForCausalLM")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hf-internal-testing/tiny-random-MambaForCausalLM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/hf-internal-testing/tiny-random-MambaForCausalLM

SGLang

How to use hf-internal-testing/tiny-random-MambaForCausalLM 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 "hf-internal-testing/tiny-random-MambaForCausalLM" \
    --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": "hf-internal-testing/tiny-random-MambaForCausalLM",
		"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 "hf-internal-testing/tiny-random-MambaForCausalLM" \
        --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": "hf-internal-testing/tiny-random-MambaForCausalLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hf-internal-testing/tiny-random-MambaForCausalLM with Docker Model Runner:
```
docker model run hf.co/hf-internal-testing/tiny-random-MambaForCausalLM
```

tiny-random-MambaForCausalLM

File size: 880 Bytes

{
  "activation_function": "silu",
  "architectures": [
    "MambaForCausalLM"
  ],
  "bos_token_id": 0,
  "conv_kernel": 4,
  "eos_token_id": 0,
  "expand": 2,
  "gradient_checkpointing": false,
  "hidden_act": "silu",
  "hidden_size": 32,
  "initializer_range": 0.1,
  "intermediate_size": 32,
  "is_decoder": true,
  "layer_norm_epsilon": 1e-05,
  "model_type": "mamba",
  "n_positions": 512,
  "num_hidden_layers": 2,
  "pad_token_id": 1023,
  "rescale_prenorm_residual": false,
  "residual_in_fp32": true,
  "state_size": 16,
  "time_step_floor": 0.0001,
  "time_step_init_scheme": "random",
  "time_step_max": 0.1,
  "time_step_min": 0.001,
  "time_step_rank": 2,
  "time_step_scale": 1.0,
  "torch_dtype": "float32",
  "transformers_version": "4.40.0.dev0",
  "type_vocab_size": 16,
  "use_bias": false,
  "use_cache": true,
  "use_conv_bias": true,
  "vocab_size": 1024
}