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llama-2-70b-QEvasion-clarity

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metadata
license: mit
datasets:
  - ailsntua/QEvasion
language:
  - en
metrics:
  - accuracy
base_model:
  - daryl149/llama-2-70b-chat-hf
library_name: transformers
pipeline_tag: text-generation

Usage 

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ailsntua/llama-2-70b-QEvasion-clarity")
model = AutoModelForCausalLM.from_pretrained("ailsntua/llama-2-70b-QEvasion-clarity")


---
base_model:
- daryl149/llama-2-70b-chat-hf
datasets:
- ailsntua/QEvasion
language:
- en
library_name: transformers
license: mit
metrics:
- accuracy
pipeline_tag: text-generation
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** AILS NTUA
- **Language(s) (NLP):** EN

### Model Sources [optional]

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- **Repository:** [More Information Needed]
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## Uses

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### Direct Use

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### Out-of-Scope Use

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

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## Training Details

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#### Training Hyperparameters

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### Results

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#### Summary



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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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