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 / data_meta.json

Omarrran

Add Transformers-compatible ks_byte_lm SpaceByte release

45121cf verified 26 days ago

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584 Bytes

	{
	"vocab_size": 259,
	"bos_id": 256,
	"eos_id": 257,
	"counts": {
	"train": 45362173,
	"val": 1622371,
	"test": 3074698
	},
	"bytes_per_word": 9.865595756933395,
	"stats": {
	"raw": 279,
	"kept": 279,
	"dropped_short": 0,
	"dropped_ksr": 0,
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	"content_bytes": 50058684,
	"words": 5074066
	},
	"normalization": {
	"zwnj_policy": "keep",
	"digit_policy": "keep_native",
	"remove_diacritics": false
	},
	"source": "Omarrran/KS-PRET-5M_5_million_kashmiri_Pretrainning_LLM_dataset_12M_tokens_2026",
	"dtype": "uint16"
	}