Instructions to use LLM-OS-Models/HRM-Text-Ko-Terminal-B-SWE-GLM-Pilot 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-B-SWE-GLM-Pilot 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-B-SWE-GLM-Pilot")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("LLM-OS-Models/HRM-Text-Ko-Terminal-B-SWE-GLM-Pilot", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LLM-OS-Models/HRM-Text-Ko-Terminal-B-SWE-GLM-Pilot 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-B-SWE-GLM-Pilot"
# 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-B-SWE-GLM-Pilot",
		"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-B-SWE-GLM-Pilot

SGLang

How to use LLM-OS-Models/HRM-Text-Ko-Terminal-B-SWE-GLM-Pilot 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-B-SWE-GLM-Pilot" \
    --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-B-SWE-GLM-Pilot",
		"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-B-SWE-GLM-Pilot" \
        --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-B-SWE-GLM-Pilot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LLM-OS-Models/HRM-Text-Ko-Terminal-B-SWE-GLM-Pilot with Docker Model Runner:
```
docker model run hf.co/LLM-OS-Models/HRM-Text-Ko-Terminal-B-SWE-GLM-Pilot
```

HRM-Text-Ko-Terminal-B-SWE-GLM-Pilot / train_metadata.yaml

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	max_seq_len: 4096
	tokenizer_info:
	boq: <\|im_start\|>
	condition_mapping:
	cot: <\|object_ref_end\|>
	direct: <\|object_ref_start\|>
	noisy: <\|quad_start\|>
	synth: <\|quad_end\|>
	eoa: <\|box_end\|>
	eoq: <\|im_end\|>
	tokenizer_path: /home/work/.data/hrm_text_prepared/sft_swe_glm_mix_v1
	total_length: 251170780
	vocab_size: 131072

	max_seq_len: 4096
	tokenizer_info:
	boq: <\|im_start\|>
	condition_mapping:
	cot: <\|object_ref_end\|>
	direct: <\|object_ref_start\|>
	noisy: <\|quad_start\|>
	synth: <\|quad_end\|>
	eoa: <\|box_end\|>
	eoq: <\|im_end\|>
	tokenizer_path: /home/work/.data/hrm_text_prepared/sft_swe_glm_mix_v1
	total_length: 251170780
	vocab_size: 131072