Instructions to use BK-Lee/Meteor-Mamba with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BK-Lee/Meteor-Mamba with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BK-Lee/Meteor-Mamba")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BK-Lee/Meteor-Mamba") model = AutoModelForCausalLM.from_pretrained("BK-Lee/Meteor-Mamba") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use BK-Lee/Meteor-Mamba with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BK-Lee/Meteor-Mamba" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BK-Lee/Meteor-Mamba", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BK-Lee/Meteor-Mamba
- SGLang
How to use BK-Lee/Meteor-Mamba 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 "BK-Lee/Meteor-Mamba" \ --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": "BK-Lee/Meteor-Mamba", "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 "BK-Lee/Meteor-Mamba" \ --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": "BK-Lee/Meteor-Mamba", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BK-Lee/Meteor-Mamba with Docker Model Runner:
docker model run hf.co/BK-Lee/Meteor-Mamba
| { | |
| "_name_or_path": "state-spaces/mamba-130m-hf", | |
| "architectures": [ | |
| "MeteorMambaForCausalLM" | |
| ], | |
| "bos_token_id": 0, | |
| "conv_kernel": 4, | |
| "d_inner": 1536, | |
| "d_model": 768, | |
| "eos_token_id": 0, | |
| "expand": 2, | |
| "fused_add_norm": true, | |
| "hidden_act": "silu", | |
| "hidden_size": 768, | |
| "ignore_index": -100, | |
| "image_token_index": 92544, | |
| "initializer_range": 0.1, | |
| "intermediate_size": 1536, | |
| "layer_norm_epsilon": 1e-05, | |
| "model_type": "mamba", | |
| "n_layer": 24, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 0, | |
| "pad_vocab_size_multiple": 8, | |
| "rescale_prenorm_residual": false, | |
| "residual_in_fp32": true, | |
| "rms_norm": true, | |
| "ssm_cfg": {}, | |
| "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": 48, | |
| "time_step_scale": 1.0, | |
| "tor_token_index": 92545, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.40.2", | |
| "use_bias": false, | |
| "use_cache": true, | |
| "use_conv_bias": true, | |
| "vocab_size": 50280 | |
| } | |