Instructions to use limloop/whiff-mamba2-20M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use limloop/whiff-mamba2-20M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="limloop/whiff-mamba2-20M") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("limloop/whiff-mamba2-20M", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use limloop/whiff-mamba2-20M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "limloop/whiff-mamba2-20M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "limloop/whiff-mamba2-20M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/limloop/whiff-mamba2-20M
- SGLang
How to use limloop/whiff-mamba2-20M 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 "limloop/whiff-mamba2-20M" \ --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": "limloop/whiff-mamba2-20M", "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 "limloop/whiff-mamba2-20M" \ --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": "limloop/whiff-mamba2-20M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use limloop/whiff-mamba2-20M with Docker Model Runner:
docker model run hf.co/limloop/whiff-mamba2-20M
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| { | |
| "architectures": [ | |
| "Mamba2ForCausalLM" | |
| ], | |
| "bos_token_id": 6, | |
| "chunk_size": 256, | |
| "conv_kernel": 4, | |
| "eos_token_id": 6, | |
| "expand": 1.5, | |
| "head_dim": 64, | |
| "hidden_act": "silu", | |
| "hidden_size": 512, | |
| "initializer_range": 0.1, | |
| "layer_norm_epsilon": 1e-05, | |
| "model_type": "mamba2", | |
| "n_groups": 2, | |
| "num_heads": 12, | |
| "num_hidden_layers": 9, | |
| "pad_token_id": 7, | |
| "rescale_prenorm_residual": false, | |
| "residual_in_fp32": true, | |
| "rms_norm": true, | |
| "state_size": 64, | |
| "tie_word_embeddings": false, | |
| "time_step_floor": 0.0001, | |
| "time_step_limit": [ | |
| 0.0, | |
| Infinity | |
| ], | |
| "time_step_max": 0.1, | |
| "time_step_min": 0.001, | |
| "time_step_rank": 32, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.51.3", | |
| "use_bias": false, | |
| "use_cache": true, | |
| "use_conv_bias": true, | |
| "vocab_size": 8192 | |
| } | |