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
- Transformers
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
File size: 338 Bytes
0d86d1b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 | 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
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