Instructions to use JunxiongWang/MambaByte_Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JunxiongWang/MambaByte_Code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="JunxiongWang/MambaByte_Code")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JunxiongWang/MambaByte_Code", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use JunxiongWang/MambaByte_Code with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JunxiongWang/MambaByte_Code" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JunxiongWang/MambaByte_Code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/JunxiongWang/MambaByte_Code
- SGLang
How to use JunxiongWang/MambaByte_Code 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 "JunxiongWang/MambaByte_Code" \ --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": "JunxiongWang/MambaByte_Code", "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 "JunxiongWang/MambaByte_Code" \ --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": "JunxiongWang/MambaByte_Code", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use JunxiongWang/MambaByte_Code with Docker Model Runner:
docker model run hf.co/JunxiongWang/MambaByte_Code
Upload 2 files
Browse filesTrain in 30B Byte. Mode size 353M.
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{"d_model": 1024, "n_layer": 56, "vocab_size": 256, "ssm_cfg": {"expand": 2}, "rms_norm": true, "residual_in_fp32": true, "fused_add_norm": true, "pad_vocab_size_multiple": 8, "tie_embeddings": false, "pad_id": 0}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:2084e62f82b19878148d413fde4f65dcf3540e9915b17aa1d7098e2aba9feada
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size 1496034106
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