Instructions to use internlm/internlm-xcomposer-7b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internlm/internlm-xcomposer-7b-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="internlm/internlm-xcomposer-7b-4bit", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("internlm/internlm-xcomposer-7b-4bit", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download quantize_config.json from internlm/internlm-xcomposer-7b-4bit: direct link, hf CLI and curl.
- Browser
- Download file 211 Bytes
-
https://huggingface.co/internlm/internlm-xcomposer-7b-4bit/resolve/main/quantize_config.json
- Command line
-
hf download hf://internlm/internlm-xcomposer-7b-4bit/quantize_config.json
-
curl -L -o quantize_config.json https://huggingface.co/internlm/internlm-xcomposer-7b-4bit/resolve/main/quantize_config.json
211 Bytes
| { | |
| "bits": 4, | |
| "group_size": 128, | |
| "damp_percent": 0.01, | |
| "desc_act": false, | |
| "static_groups": false, | |
| "sym": true, | |
| "true_sequential": true, | |
| "model_name_or_path": null, | |
| "model_file_base_name": null | |
| } |