Instructions to use 0xSero/GLM-4.7-Flash-Tools with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 0xSero/GLM-4.7-Flash-Tools with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="0xSero/GLM-4.7-Flash-Tools") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("0xSero/GLM-4.7-Flash-Tools", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use 0xSero/GLM-4.7-Flash-Tools with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "0xSero/GLM-4.7-Flash-Tools" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "0xSero/GLM-4.7-Flash-Tools", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/0xSero/GLM-4.7-Flash-Tools
- SGLang
How to use 0xSero/GLM-4.7-Flash-Tools 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 "0xSero/GLM-4.7-Flash-Tools" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "0xSero/GLM-4.7-Flash-Tools", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "0xSero/GLM-4.7-Flash-Tools" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "0xSero/GLM-4.7-Flash-Tools", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use 0xSero/GLM-4.7-Flash-Tools with Docker Model Runner:
docker model run hf.co/0xSero/GLM-4.7-Flash-Tools
| { | |
| "backend": "tokenizers", | |
| "clean_up_tokenization_spaces": false, | |
| "do_lower_case": false, | |
| "eos_token": "<|endoftext|>", | |
| "extra_special_tokens": [ | |
| "<|endoftext|>", | |
| "[MASK]", | |
| "[gMASK]", | |
| "[sMASK]", | |
| "<sop>", | |
| "<eop>", | |
| "<|system|>", | |
| "<|user|>", | |
| "<|assistant|>", | |
| "<|observation|>", | |
| "<|begin_of_image|>", | |
| "<|end_of_image|>", | |
| "<|begin_of_video|>", | |
| "<|end_of_video|>", | |
| "<|begin_of_audio|>", | |
| "<|end_of_audio|>", | |
| "<|begin_of_transcription|>", | |
| "<|end_of_transcription|>" | |
| ], | |
| "is_local": false, | |
| "model_max_length": 128000, | |
| "model_specific_special_tokens": {}, | |
| "pad_token": "<|endoftext|>", | |
| "padding_side": "left", | |
| "remove_space": false, | |
| "tokenizer_class": "TokenizersBackend" | |
| } | |