Instructions to use Q-bert/Mamba-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Q-bert/Mamba-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Q-bert/Mamba-1B", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Q-bert/Mamba-1B", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Q-bert/Mamba-1B", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Q-bert/Mamba-1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Q-bert/Mamba-1B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Q-bert/Mamba-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Q-bert/Mamba-1B
- SGLang
How to use Q-bert/Mamba-1B 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 "Q-bert/Mamba-1B" \ --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": "Q-bert/Mamba-1B", "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 "Q-bert/Mamba-1B" \ --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": "Q-bert/Mamba-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Q-bert/Mamba-1B with Docker Model Runner:
docker model run hf.co/Q-bert/Mamba-1B
Upload folder using huggingface_hub
Browse files- config.json +20 -0
- generation_config.json +4 -0
- pytorch_model.bin +3 -0
config.json
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{
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"architectures": [
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"MambaForCausalLM"
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],
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"bias": false,
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"conv_bias": true,
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"d_conv": 4,
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"d_inner": 4096,
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"d_model": 2048,
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"d_state": 16,
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"dt_rank": 128,
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"expand": 2,
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"initializer_range": 0.02,
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"model_type": "mamba",
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"n_layer": 48,
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"pad_vocab_size_multiple": 8,
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"torch_dtype": "float32",
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"transformers_version": "4.35.2",
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"vocab_size": 50280
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}
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generation_config.json
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{
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"_from_model_config": true,
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"transformers_version": "4.35.2"
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:fc8d09574c5cc76526a1d98e2006df69bfdb28f3081bbf910eb656ceaff6a899
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size 5488766318
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