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
- Transformers
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
File size: 1,467 Bytes
857a0d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | {
"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"
]
}
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