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library_name: transformers
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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library_name: transformers
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license: other
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**This model is made with the intention to be used for fine-tuning. It should not to be used for inference as is.**
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This is a pruned version of [Meta-Llama-3-70B-Instruct](https://huggingface.co/Meta-Llama-3-70B-Instruct) .
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[Meta-Llama-3-70B-Instruct](https://huggingface.co/Meta-Llama-3-70B-Instruct) has 70.6 billion params and Drobeta-Turnu-Severin has 44.9 billion (~63% param size)
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# Steps to replicate:
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Use [laserQlora.ipynb](https://github.com/cognitivecomputations/laserRMT/blob/main/laserQlora.ipynb) from [cognitivecomputations/laserRMT](https://github.com/cognitivecomputations/laserRMT) to determine which layers should be eliminated.
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Adapt the script for `Meta-Llama-3-70B-Instruct` by replacing `model_name = "mistralai/Mistral-7B-v0.1"` with `model_name = "Meta-Llama-3-70B-Instruct"` and `layer_numbers = list(range(31, -1, -1))` with `layer_numbers = list(range(79, -1, -1))`, [79 being the last recurrent layer index Meta-Llama-3-70B-Instruct has](https://huggingface.co/Meta-Llama-3-70B-Instruct?show_tensors=true).
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Then look for the layer indexes where self_attn.v_proj snr is Infinity and eliminate those layers using [mergekit](https://github.com/arcee-ai/mergekit).
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Here are the layer indexes that were eliminated: 11,17,37,40,41,42,43,44,45,46,48,49,50,51,53,54,55,57,58,59,60,61,62,63,64,65,66,67,68,69 .
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Here is the mergekit config:
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```yml
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slices:
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- sources:
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- model: "meta-llama/Meta-Llama-3-70B-Instruct"
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layer_range: [0, 11]
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- sources:
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- model: "meta-llama/Meta-Llama-3-70B-Instruct"
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layer_range: [12, 17]
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- sources:
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- model: "meta-llama/Meta-Llama-3-70B-Instruct"
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layer_range: [18, 37]
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- sources:
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- model: "meta-llama/Meta-Llama-3-70B-Instruct"
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layer_range: [38, 40]
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- sources:
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- model: "meta-llama/Meta-Llama-3-70B-Instruct"
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layer_range: [47, 48]
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- sources:
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- model: "meta-llama/Meta-Llama-3-70B-Instruct"
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layer_range: [52, 53]
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- sources:
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- model: "meta-llama/Meta-Llama-3-70B-Instruct"
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layer_range: [56, 57]
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- sources:
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- model: "meta-llama/Meta-Llama-3-70B-Instruct"
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layer_range: [70, 80]
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merge_method: passthrough
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dtype: bfloat16
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```
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