Instructions to use Omarrran/ks-byte-lm-spacebyte-transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Omarrran/ks-byte-lm-spacebyte-transformers with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Omarrran/ks-byte-lm-spacebyte-transformers", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Omarrran/ks-byte-lm-spacebyte-transformers", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Omarrran/ks-byte-lm-spacebyte-transformers with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Omarrran/ks-byte-lm-spacebyte-transformers"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Omarrran/ks-byte-lm-spacebyte-transformers",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Omarrran/ks-byte-lm-spacebyte-transformers

SGLang

How to use Omarrran/ks-byte-lm-spacebyte-transformers 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 "Omarrran/ks-byte-lm-spacebyte-transformers" \
    --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": "Omarrran/ks-byte-lm-spacebyte-transformers",
		"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 "Omarrran/ks-byte-lm-spacebyte-transformers" \
        --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": "Omarrran/ks-byte-lm-spacebyte-transformers",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Omarrran/ks-byte-lm-spacebyte-transformers with Docker Model Runner:
```
docker model run hf.co/Omarrran/ks-byte-lm-spacebyte-transformers
```

ks-byte-lm-spacebyte-transformers / tokenizer_config.json

Omarrran

Add Transformers-compatible ks_byte_lm SpaceByte release

45121cf verified 29 days ago

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	{
	"added_tokens_decoder": {
	"256": {
	"content": "<bos>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"257": {
	"content": "<eos>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	},
	"258": {
	"content": "<pad>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false,
	"special": true
	}
	},
	"bos_token": "<bos>",
	"clean_up_tokenization_spaces": false,
	"eos_token": "<eos>",
	"extra_special_tokens": {},
	"model_max_length": 2048,
	"pad_token": "<pad>",
	"tokenizer_class": "KsByteTokenizer",
	"unk_token": "<pad>",
	"auto_map": {
	"AutoTokenizer": [
	"tokenization_ksbyte.KsByteTokenizer",
	null
	]
	},
	"zwnj_policy": "keep",
	"digit_policy": "keep_native",
	"remove_diacritics": false
	}