Visual Question Answering
Transformers
PyTorch
internlm2
feature-extraction
custom_code
4-bit precision
awq
Instructions to use internlm/internlm-xcomposer2d5-7b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internlm/internlm-xcomposer2d5-7b-4bit with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "visual-question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("visual-question-answering", model="internlm/internlm-xcomposer2d5-7b-4bit", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("internlm/internlm-xcomposer2d5-7b-4bit", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download generation_config.json from internlm/internlm-xcomposer2d5-7b-4bit: direct link, hf CLI and curl.
- Browser
- Download file 177 Bytes
-
https://huggingface.co/internlm/internlm-xcomposer2d5-7b-4bit/resolve/main/generation_config.json
- Command line
-
hf download hf://internlm/internlm-xcomposer2d5-7b-4bit/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/internlm/internlm-xcomposer2d5-7b-4bit/resolve/main/generation_config.json
177 Bytes
| { | |
| "_from_model_config": true, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "max_length": 16384, | |
| "pad_token_id": 2, | |
| "transformers_version": "4.33.1", | |
| "use_cache": false | |
| } | |