README.md

---
library_name: transformers
tags:
- 'model: llama'
- 'repo_name: llama_channel_4_date_understanding_All'
- 'file_name: llama_channel_4_date_understanding_All_5000_5.pt'
- 'base_model: meta-llama/Llama-2-7b-hf'
- 'pruning_style: channel'
- 'community: 4'
- 'pruning_ratio: 20'
- 'dataset_label: date_understanding'
- 'sparsity_ratio: 20'
- 'dataset: [''tasksource/bigbench'', ''date_understanding'']'
- 'finetune: All'
- 'modules_size: 17'
- 'modules: [''13_attn.k'', ''13_attn.v'', ''14_attn.k'', ''16_gate'', ''16_mlp.up'',
  ''17_attn.v'', ''19_attn.o'', ''23_mlp.down'', ''24_attn.v'', ''25_attn.v'', ''25_mlp.up'',
  ''28_attn.k'', ''29_mlp.down'', ''3_mlp.up'', ''9_attn.k'', ''9_attn.o'', ''9_mlp.up'']'
- 'rank: 1'
- 'tags: [''model: llama'', ''repo_name: llama_channel_4_date_understanding_All'',
  ''file_name: llama_channel_4_date_understanding_All_5000_5.pt'', ''base_model: meta-llama/Llama-2-7b-hf'',
  ''pruning_style: channel'', ''community: 4'', ''pruning_ratio: 20'', ''dataset_label:
  date_understanding'', ''sparsity_ratio: 20'', "dataset: [''tasksource/bigbench'',
  ''date_understanding'']", ''finetune: All'', ''modules_size: 17'', "modules: [''13_attn.k'',
  ''13_attn.v'', ''14_attn.k'', ''16_gate'', ''16_mlp.up'', ''17_attn.v'', ''19_attn.o'',
  ''23_mlp.down'', ''24_attn.v'', ''25_attn.v'', ''25_mlp.up'', ''28_attn.k'', ''29_mlp.down'',
  ''3_mlp.up'', ''9_attn.k'', ''9_attn.o'', ''9_mlp.up'']", ''rank: 1'']'
---

# Model Card for Model ID

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## 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.

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## Uses

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### Direct Use

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### Recommendations

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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

### Training Data

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### Training Procedure

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#### Training Hyperparameters

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## Evaluation

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#### Summary



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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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