predibase
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tldr_headline_gen

Text Generation

PEFT

Safetensors

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----
-library_name: peft
-base_model: mistralai/Mistral-7B-v0.1
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-## Uses
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
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-#### Training Hyperparameters
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-## Evaluation
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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-### Framework versions
-- PEFT 0.7.1

+---\
+library_name: peft\
+Base model: mistralai/Mistral-7B-v0.1\
+pipeline_tag: text-generation\
+---\
+Description: Headline generation given news content\
+Original dataset: tldr_headline_gen \
+---\
+Try querying this adapter for free in Lora Land at https://predibase.com/lora-land! \
+The adapter_category is Summarization and the name is News Headline Generation (tldr_news)\
+---\
+Sample input: The following passage is content from a news report. Please summarize this passage in one sentence or less.\n\nPassage: Jeffrey Berns, CEO of Blockchains LLC, wants the Nevada government to allow companies like his to form local governments on land they own, granting them power over everything from schools to law enforcement. Berns envisions a city based on digital currencies and blockchain storage. His company is proposing to build a 15,000 home town 12 miles east of Reno. Nevada Lawmakers have responded with intrigue and skepticism. The proposed legislation has yet to be formally filed or discussed in public hearings.\n\nSummary: \
+---\
+Sample output: Blockchain CEO wants to build a city in Nevada \
+---\
+Try using this adapter yourself!
+```
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "mistralai/Mistral-7B-v0.1"
+peft_model_id = "predibase/tldr_headline_gen"
+model = AutoModelForCausalLM.from_pretrained(model_id)
+model.load_adapter(peft_model_id)
+```