Instructions to use dingodb/chatglm-tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dingodb/chatglm-tuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dingodb/chatglm-tuning", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dingodb/chatglm-tuning", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| from transformers import AutoTokenizer, AutoModel | |
| tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True) | |
| model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True, device='cuda') | |
| model = model.eval() | |
| response, history = model.chat(tokenizer, "你好", history=[]) | |
| print(response) | |
| response, history = model.chat(tokenizer, "晚上睡不着应该怎么办", history=history) | |
| print(response) |