README.md

---
library_name: transformers
tags:
- 'model: llama'
- 'repo_name: llama_block_1_language_identification_Community'
- 'file_name: llama_block_1_language_identification_Community_5000_5.pt'
- 'base_model: meta-llama/Llama-2-7b-hf'
- 'pruning_style: block'
- 'community: 1'
- 'pruning_ratio: 20'
- 'dataset_label: language_identification'
- 'sparsity_ratio: 20'
- 'dataset: [''tasksource/bigbench'', ''language_identification'']'
- 'finetune: Community'
- 'modules_size: 27'
- 'modules: [''10_attn.v'', ''12_attn.q'', ''12_attn.v'', ''14_attn.q'', ''16_attn.k'',
  ''17_attn.q'', ''17_attn.v'', ''18_attn.o'', ''20_attn.k'', ''21_attn.o'', ''22_attn.k'',
  ''22_attn.o'', ''24_attn.q'', ''26_attn.q'', ''28_attn.k'', ''28_attn.q'', ''28_attn.v'',
  ''29_attn.q'', ''30_attn.k'', ''3_attn.k'', ''3_attn.v'', ''4_attn.o'', ''6_attn.k'',
  ''6_attn.v'', ''7_attn.o'', ''9_attn.k'', ''9_attn.v'']'
- 'rank: 1'
- 'tags: [''model: llama'', ''repo_name: llama_block_1_language_identification_Community'',
  ''file_name: llama_block_1_language_identification_Community_5000_5.pt'', ''base_model:
  meta-llama/Llama-2-7b-hf'', ''pruning_style: block'', ''community: 1'', ''pruning_ratio:
  20'', ''dataset_label: language_identification'', ''sparsity_ratio: 20'', "dataset:
  [''tasksource/bigbench'', ''language_identification'']", ''finetune: Community'',
  ''modules_size: 27'', "modules: [''10_attn.v'', ''12_attn.q'', ''12_attn.v'', ''14_attn.q'',
  ''16_attn.k'', ''17_attn.q'', ''17_attn.v'', ''18_attn.o'', ''20_attn.k'', ''21_attn.o'',
  ''22_attn.k'', ''22_attn.o'', ''24_attn.q'', ''26_attn.q'', ''28_attn.k'', ''28_attn.q'',
  ''28_attn.v'', ''29_attn.q'', ''30_attn.k'', ''3_attn.k'', ''3_attn.v'', ''4_attn.o'',
  ''6_attn.k'', ''6_attn.v'', ''7_attn.o'', ''9_attn.k'', ''9_attn.v'']", ''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

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#### Training Hyperparameters

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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).

- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]

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