Instructions to use huihui-ai/Huihui-Hunyuan-MT-Chimera-7B-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huihui-ai/Huihui-Hunyuan-MT-Chimera-7B-abliterated with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" 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("translation", model="huihui-ai/Huihui-Hunyuan-MT-Chimera-7B-abliterated")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huihui-ai/Huihui-Hunyuan-MT-Chimera-7B-abliterated") model = AutoModelForCausalLM.from_pretrained("huihui-ai/Huihui-Hunyuan-MT-Chimera-7B-abliterated", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a1b9a9f95f0b03d30aa31d24b2a2f4e4beea55ba02f3948ed60cdcf1bbc516be
- Size of remote file:
- 4.98 GB
- SHA256:
- 7dbe79952d8f070ba4948f0cdab66a92e3cad678fd9847ad2f28c10c9fb0977c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.