Question Answering
Transformers
Safetensors
English
mistral
text-generation
PEFT
sft
TensorBoard
Safetensors
trl
generated_from_trainer 4-bit
precision
text-generation-inference
4-bit precision
gptq
Instructions to use Feluda/Zephyr-7b-QnA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Feluda/Zephyr-7b-QnA with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "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("question-answering", model="Feluda/Zephyr-7b-QnA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Feluda/Zephyr-7b-QnA") model = AutoModelForCausalLM.from_pretrained("Feluda/Zephyr-7b-QnA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from Feluda/Zephyr-7b-QnA: direct link, hf CLI and curl.
- Browser
- Download file 493 kB
-
https://huggingface.co/Feluda/Zephyr-7b-QnA/resolve/main/tokenizer.model
- Command line
-
hf download hf://Feluda/Zephyr-7b-QnA/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/Feluda/Zephyr-7b-QnA/resolve/main/tokenizer.model
493 kB
- Xet hash:
- 1e090c2d2774ea7875da72d682c12600bd69085e9c28674b917a49fe82ccffe2
- Size of remote file:
- 493 kB
- SHA256:
- dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
路
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