Text Generation
Transformers
Safetensors
English
nsa
sparse-attention
117m
conversational
custom_code
Instructions to use seconds-0/nsa-117m-byte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seconds-0/nsa-117m-byte with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="seconds-0/nsa-117m-byte", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("seconds-0/nsa-117m-byte", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use seconds-0/nsa-117m-byte with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "seconds-0/nsa-117m-byte" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "seconds-0/nsa-117m-byte", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/seconds-0/nsa-117m-byte
- SGLang
How to use seconds-0/nsa-117m-byte 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 "seconds-0/nsa-117m-byte" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "seconds-0/nsa-117m-byte", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "seconds-0/nsa-117m-byte" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "seconds-0/nsa-117m-byte", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use seconds-0/nsa-117m-byte with Docker Model Runner:
docker model run hf.co/seconds-0/nsa-117m-byte
| { | |
| "model_type": "nsa", | |
| "architectures": [ | |
| "NSAForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_nsa.NSAConfig", | |
| "AutoModelForCausalLM": "modeling_nsa.NSAForCausalLM" | |
| }, | |
| "vocab_size": 256, | |
| "hidden_size": 768, | |
| "num_hidden_layers": 12, | |
| "num_attention_heads": 12, | |
| "n_kv_groups": 2, | |
| "d_k": 64, | |
| "d_v": 64, | |
| "max_position_embeddings": 2048, | |
| "rope_theta": 10000, | |
| "nsa": { | |
| "branches": [ | |
| "cmp", | |
| "sel", | |
| "win" | |
| ], | |
| "window": 512, | |
| "gqa_groups": 2, | |
| "block": 32, | |
| "stride": 16, | |
| "sel_block": 64, | |
| "sel_top_n": 16 | |
| } | |
| } |