Instructions to use seconds-0/nsa-117m-byte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

nsa-117m-byte / tokenization_nsa.py

seconds-0

NSA 117M initial export

4303959 verified 11 months ago

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2.98 kB

	# Remote code: configuration and modeling for NSA
	from typing import List, Optional, Dict
	import json
	from transformers import PreTrainedTokenizer


	class NSAByteTokenizer(PreTrainedTokenizer):
	"""A simple byte-level tokenizer with fixed vocab size 256.

	- Encodes UTF-8 bytes of the input string as token ids 0..255.
	- No special tokens by default; EOS/PAD can be configured via special tokens map.
	- Decoding uses UTF-8 with replacement for invalid sequences.
	"""

	def __init__(self, **kwargs):
	# Build a stable 256-entry vocab mapping before base init (base may query the vocab)
	self._vocab: Dict[str, int] = {f"<{i}>": i for i in range(256)}
	self._ids_to_tokens: Dict[int, str] = {i: f"<{i}>" for i in range(256)}
	super().__init__(**kwargs)
	# Only return input_ids and attention_mask to avoid unused token_type_ids in generation
	self.model_input_names = ["input_ids", "attention_mask"]

	@property
	def vocab_size(self) -> int: # type: ignore[override]
	return 256

	def get_vocab(self) -> Dict[str, int]: # type: ignore[override]
	return dict(self._vocab)

	def _tokenize(self, text: str) -> List[str]: # type: ignore[override]
	data = text.encode("utf-8", errors="replace")
	return [f"<{b}>" for b in data]

	def _convert_token_to_id(self, token: str) -> int: # type: ignore[override]
	if token in self._vocab:
	return self._vocab[token]
	# Fallback: try parse numeric inside <..>
	if token.startswith("<") and token.endswith(">"):
	try:
	v = int(token[1:-1])
	if 0 <= v < 256:
	return v
	except Exception:
	pass
	return 0

	def _convert_id_to_token(self, index: int) -> str: # type: ignore[override]
	return self._ids_to_tokens.get(int(index) % 256, "<0>")

	def convert_tokens_to_string(self, tokens: List[str]) -> str: # type: ignore[override]
	bs = []
	for t in tokens:
	if t in self._vocab:
	bs.append(self._vocab[t])
	else:
	try:
	if t.startswith("<") and t.endswith(">"):
	v = int(t[1:-1])
	if 0 <= v < 256:
	bs.append(v)
	continue
	except Exception:
	pass
	return bytes(bs).decode("utf-8", errors="replace")

	def build_inputs_with_special_tokens(self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) -> List[int]: # type: ignore[override]
	if token_ids_1 is None:
	return token_ids_0
	return token_ids_0 + token_ids_1

	def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None): # type: ignore[override]
	# Nothing to save besides special tokens map handled by the base class.
	return (), ()