Question Answering
Transformers
Safetensors
English
mistral
text-generation
Eval Results (legacy)
text-generation-inference
Instructions to use eren23/DistilHermes-2.5-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eren23/DistilHermes-2.5-Mistral-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="eren23/DistilHermes-2.5-Mistral-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("eren23/DistilHermes-2.5-Mistral-7B") model = AutoModelForCausalLM.from_pretrained("eren23/DistilHermes-2.5-Mistral-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a1f36dd2988504d08cb6700f5d8a714d4ca0ee762578110639a7ad3700a40b3b
- Size of remote file:
- 5 GB
- SHA256:
- 4d760197d26ce4feda2533f51480af75a4db291d6d14de8919a75337e7221758
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.