Edit model card

Description: Question answering given a passage
Original dataset: https://huggingface.co/datasets/drop
---
Try querying this adapter for free in Lora Land at https://predibase.com/lora-land!
The adapter_category is Other and the name is Question Answering (drop)
---
Sample input: Given a passage, you need to accurately identify and extract relevant spans of text that answer specific questions. Provide concise and coherent responses based on the information present in the passage.\n\n### Passage: Coming off their home win over the Browns, the Ravens flew to Heinz Field for their first road game of the year, as they played a Week 4 MNF duel with the throwback-clad Pittsburgh Steelers. In the first quarter, Baltimore trailed early as Steelers kicker Jeff Reed got a 49-yard field goal. The Ravens responded with kicker Matt Stover getting a 33-yard field goal. Baltimore gained the lead in the second quarter as Stover kicked a 20-yard field goal, while rookie quarterback Joe Flacco completed his first career touchdown pass as he hooked up with TE Daniel Wilcox from 4 yards out. In the third quarter, Pittsburgh took the lead with quarterback Ben Roethlisberger completing a 38-yard TD pass to WR Santonio Holmes, along with LB James Harrison forcing a fumble from Flacco with LB LaMarr Woodley returning the fumble 7 yards for a touchdown. In the fourth quarter, the Steelers increased their lead with Reed getting a 19-yard field goal. Afterwards, the Ravens tied the game with RB Le'Ron McClain getting a 2-yard TD run. However, despite winning the coin toss in overtime, Baltimore was unable to gain ground. In the end, Pittsburgh sealed Baltimore's fate as Reed nailed the game-winning 46-yard field goal.\n### Question: How many more field goals were made in the first half than in the second?\n### Answer:
---
Sample output: 1
---
Try using this adapter yourself!

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "mistralai/Mistral-7B-v0.1"
peft_model_id = "predibase/drop"

model = AutoModelForCausalLM.from_pretrained(model_id)
model.load_adapter(peft_model_id)
Downloads last month
173
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for predibase/drop

Adapter
(1164)
this model

Collection including predibase/drop