Instructions to use ranchlai/chatglm3-6B-gptq-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ranchlai/chatglm3-6B-gptq-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ranchlai/chatglm3-6B-gptq-4bit", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ranchlai/chatglm3-6B-gptq-4bit", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ranchlai/chatglm3-6B-gptq-4bit: direct link, hf CLI and curl.
- Browser
- Download file 4.37 GB
-
https://huggingface.co/ranchlai/chatglm3-6B-gptq-4bit/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ranchlai/chatglm3-6B-gptq-4bit/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ranchlai/chatglm3-6B-gptq-4bit/resolve/main/pytorch_model.bin
4.37 GB
- Xet hash:
- 5427cd9014fe546d98a16aca3417c4c31c1768ca50082bcd2d1ce6283d2075b9
- Size of remote file:
- 4.37 GB
- SHA256:
- 8127e0974f1ceb474eeea36b371685df874f6bc30e1bf92c71da07969eebfbdc
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