Instructions to use LLM-OS-Models/HRM-Text-Ko-Terminal-Tokenizer-131K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM-OS-Models/HRM-Text-Ko-Terminal-Tokenizer-131K with Transformers:

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

pipe = pipeline("text-generation", model="LLM-OS-Models/HRM-Text-Ko-Terminal-Tokenizer-131K")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("LLM-OS-Models/HRM-Text-Ko-Terminal-Tokenizer-131K", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LLM-OS-Models/HRM-Text-Ko-Terminal-Tokenizer-131K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLM-OS-Models/HRM-Text-Ko-Terminal-Tokenizer-131K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLM-OS-Models/HRM-Text-Ko-Terminal-Tokenizer-131K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LLM-OS-Models/HRM-Text-Ko-Terminal-Tokenizer-131K

SGLang

How to use LLM-OS-Models/HRM-Text-Ko-Terminal-Tokenizer-131K 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 "LLM-OS-Models/HRM-Text-Ko-Terminal-Tokenizer-131K" \
    --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": "LLM-OS-Models/HRM-Text-Ko-Terminal-Tokenizer-131K",
		"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 "LLM-OS-Models/HRM-Text-Ko-Terminal-Tokenizer-131K" \
        --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": "LLM-OS-Models/HRM-Text-Ko-Terminal-Tokenizer-131K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LLM-OS-Models/HRM-Text-Ko-Terminal-Tokenizer-131K with Docker Model Runner:
```
docker model run hf.co/LLM-OS-Models/HRM-Text-Ko-Terminal-Tokenizer-131K
```

HRM-Text-Ko-Terminal-Tokenizer-131K / tokenizer_training_manifest.json

gyung

Upload HRM Ko-Terminal tokenizer v1

857a0d8 verified 1 day ago

raw

history blame contribute delete

1.47 kB

	{
	"vocab_size": 131072,
	"requested_vocab_size": 131072,
	"max_gib": 2.5,
	"max_mib_per_input": 256.0,
	"min_frequency": 2,
	"special_tokens": [
	"<\|PAD\|>",
	"<\|unk\|>",
	"<\|im_start\|>",
	"<\|im_end\|>",
	"<\|system\|>",
	"<\|user\|>",
	"<\|assistant\|>",
	"<\|tool_call\|>",
	"<\|/tool_call\|>",
	"<\|tool_response\|>",
	"<\|function\|>",
	"<\|/function\|>",
	"<\|execute\|>",
	"<\|result\|>",
	"<\|terminal\|>",
	"<\|/terminal\|>",
	"<\|command\|>",
	"<\|output\|>",
	"<\|error\|>",
	"<\|exit_code\|>",
	"<\|json_start\|>",
	"<\|json_end\|>",
	"<\|xml_start\|>",
	"<\|xml_end\|>",
	"<\|code_start\|>",
	"<\|code_end\|>",
	"<think>",
	"</think>",
	"<\|direct\|>",
	"<\|cot\|>",
	"<\|noisy\|>",
	"<\|synth\|>",
	"<\|object_ref_start\|>",
	"<\|object_ref_end\|>",
	"<\|box_start\|>",
	"<\|box_end\|>",
	"<\|quad_start\|>",
	"<\|quad_end\|>",
	"<\|vision_start\|>",
	"<\|vision_end\|>",
	"<\|vision_pad\|>",
	"<\|image_pad\|>",
	"<\|video_pad\|>",
	"<\|fim_prefix\|>",
	"<\|fim_middle\|>",
	"<\|fim_suffix\|>"
	],
	"inputs": [
	"HRM-Text/legalize-kr",
	"HRM-Text/ordinance-kr",
	"admrule-kr",
	"precedent-kr",
	"dataset",
	"HRM-Text/data_toolbench/data",
	"/home/work/.data/huggingface/hrm_text_extra/sft",
	"/home/work/.data/huggingface/hrm_text_extra/tokenizer_corpus",
	"/home/work/.data/huggingface/hrm_text_extra/raw/angrygiraffe__claude-opus-4.6-4.7-reasoning-8.7k"
	]
	}

	{
	"vocab_size": 131072,
	"requested_vocab_size": 131072,
	"max_gib": 2.5,
	"max_mib_per_input": 256.0,
	"min_frequency": 2,
	"special_tokens": [
	"<\|PAD\|>",
	"<\|unk\|>",
	"<\|im_start\|>",
	"<\|im_end\|>",
	"<\|system\|>",
	"<\|user\|>",
	"<\|assistant\|>",
	"<\|tool_call\|>",
	"<\|/tool_call\|>",
	"<\|tool_response\|>",
	"<\|function\|>",
	"<\|/function\|>",
	"<\|execute\|>",
	"<\|result\|>",
	"<\|terminal\|>",
	"<\|/terminal\|>",
	"<\|command\|>",
	"<\|output\|>",
	"<\|error\|>",
	"<\|exit_code\|>",
	"<\|json_start\|>",
	"<\|json_end\|>",
	"<\|xml_start\|>",
	"<\|xml_end\|>",
	"<\|code_start\|>",
	"<\|code_end\|>",
	"<think>",
	"</think>",
	"<\|direct\|>",
	"<\|cot\|>",
	"<\|noisy\|>",
	"<\|synth\|>",
	"<\|object_ref_start\|>",
	"<\|object_ref_end\|>",
	"<\|box_start\|>",
	"<\|box_end\|>",
	"<\|quad_start\|>",
	"<\|quad_end\|>",
	"<\|vision_start\|>",
	"<\|vision_end\|>",
	"<\|vision_pad\|>",
	"<\|image_pad\|>",
	"<\|video_pad\|>",
	"<\|fim_prefix\|>",
	"<\|fim_middle\|>",
	"<\|fim_suffix\|>"
	],
	"inputs": [
	"HRM-Text/legalize-kr",
	"HRM-Text/ordinance-kr",
	"admrule-kr",
	"precedent-kr",
	"dataset",
	"HRM-Text/data_toolbench/data",
	"/home/work/.data/huggingface/hrm_text_extra/sft",
	"/home/work/.data/huggingface/hrm_text_extra/tokenizer_corpus",
	"/home/work/.data/huggingface/hrm_text_extra/raw/angrygiraffe__claude-opus-4.6-4.7-reasoning-8.7k"
	]
	}