Instructions to use Dans-DiscountModels/Dans-07YahooAnswers-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dans-DiscountModels/Dans-07YahooAnswers-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Dans-DiscountModels/Dans-07YahooAnswers-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Dans-DiscountModels/Dans-07YahooAnswers-7b") model = AutoModelForCausalLM.from_pretrained("Dans-DiscountModels/Dans-07YahooAnswers-7b") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
datasets:
- PocketDoc/Retro-YahooAnswers
language:
- en
pipeline_tag: question-answering
base_model: mistralai/Mistral-7B-v0.1
Description
Do you miss the vibes of the early 2000s? Yearn for the nostalgia of internet religious arguments? Then this model is for you!
This was trained on a scrape of Yahoo! Answers from 2007 and received no filtering save for basic sanity checks.
This is not intended for serious use but I think it's charming in a way.
Prompt format:
Pygmalion / Metharme
The prompt should start with the cursor on the same line directly after "<|model|>" with no space. The following are all valid formats and can be extended to as many rounds as desired.
<|system|>system message here<|user|>user message here<|model|>
<|system|>system message here<|user|>user message here<|model|>model message<|user|>user message here<|model|>
<|system|>system message here<|model|>
<|system|>system message here<|model|>model message<|user|>user message here<|model|>