Instructions to use mlsquare/130M_Seshu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlsquare/130M_Seshu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mlsquare/130M_Seshu")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mlsquare/130M_Seshu") model = AutoModelForCausalLM.from_pretrained("mlsquare/130M_Seshu") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use mlsquare/130M_Seshu with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mlsquare/130M_Seshu" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlsquare/130M_Seshu", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mlsquare/130M_Seshu
- SGLang
How to use mlsquare/130M_Seshu 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 "mlsquare/130M_Seshu" \ --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": "mlsquare/130M_Seshu", "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 "mlsquare/130M_Seshu" \ --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": "mlsquare/130M_Seshu", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mlsquare/130M_Seshu with Docker Model Runner:
docker model run hf.co/mlsquare/130M_Seshu
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899c998 0f450c0 899c998 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"_name_or_path": "mlsquare/130M_Seshu",
"architectures": [
"MambaForCausalLM"
],
"bias": false,
"conv_bias": true,
"d_conv": 4,
"d_inner": 5120,
"d_model": 2560,
"d_state": 16,
"dt_rank": 160,
"expand": 2,
"initializer_range": 0.02,
"model_type": "mamba",
"n_layer": 64,
"pad_vocab_size_multiple": 8,
"torch_dtype": "float32",
"transformers_version": "4.38.1",
"vocab_size": 20000
}
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