Instructions to use yujiepan/jamba-tiny-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yujiepan/jamba-tiny-random with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="yujiepan/jamba-tiny-random", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("yujiepan/jamba-tiny-random", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("yujiepan/jamba-tiny-random", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use yujiepan/jamba-tiny-random with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yujiepan/jamba-tiny-random" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yujiepan/jamba-tiny-random", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/yujiepan/jamba-tiny-random
- SGLang
How to use yujiepan/jamba-tiny-random 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 "yujiepan/jamba-tiny-random" \ --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": "yujiepan/jamba-tiny-random", "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 "yujiepan/jamba-tiny-random" \ --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": "yujiepan/jamba-tiny-random", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use yujiepan/jamba-tiny-random with Docker Model Runner:
docker model run hf.co/yujiepan/jamba-tiny-random
Upload folder using huggingface_hub
Browse files- config.json +2 -2
- model.safetensors +2 -2
config.json
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"mamba_conv_bias": true,
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"mamba_d_conv": 4,
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"mamba_d_state": 16,
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"mamba_dt_rank":
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"mamba_expand": 2,
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"mamba_inner_layernorms": true,
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"mamba_proj_bias": false,
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"num_attention_heads": 4,
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"num_experts": 16,
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"num_experts_per_tok": 2,
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"num_hidden_layers":
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"num_key_value_heads": 2,
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"output_router_logits": false,
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"pad_token_id": 0,
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"mamba_conv_bias": true,
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"mamba_d_conv": 4,
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"mamba_d_state": 16,
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"mamba_dt_rank": 256,
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"mamba_expand": 2,
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"mamba_inner_layernorms": true,
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"mamba_proj_bias": false,
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"num_attention_heads": 4,
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"num_experts": 16,
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"num_experts_per_tok": 2,
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"num_hidden_layers": 16,
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"num_key_value_heads": 2,
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"output_router_logits": false,
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"pad_token_id": 0,
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:b6ab01c6269c51bc084de15f4b817241cfa38699b5775f4f290889e9ac8f6ef9
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size 1274744
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