---
base_model: meta-llama/Llama-2-7b-hf
library_name: peftf
---

# Model Card for vantaa32/llama-2-7b-fourierft-alpaca

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

meta-llama/Llama-2-7b-hf fine-tuned on alpaca-cleaned dataset using FourierFT

Fine-tuned with hyperparameters specified in the paper + weight decay of 2e-3

## Model Details

### Model Description

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- **Developed by:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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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

Use the code below to get started with the model.

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
```sh
export NCCL_IB_DISABLE=1
export NCCL_P2P_DISABLE=1
python SFT.py \
    --model_tag llama-2-7b \
    --model_name_or_path meta-llama/Llama-2-7b-hf \
    --n_frequency 1000 \
    --num_train_epochs 1 \
    --weight_decay 2e-3 \
    --learning_rate 0.03 \
    --scale 300.0 \
    --per_device_train_batch_size 4 \
    --gradient_accumulation_steps 4 \
    --warmup_ratio 0.06 \
    --output_dir output-peft-lib
```

#### Speeds, Sizes, Times [optional]

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## Evaluation

### MT-Bench 3.789308

### MMLU: 0.457
```
Average accuracy 0.320 - abstract_algebra
Average accuracy 0.444 - anatomy
Token indices sequence length is longer than the specified maximum sequence length for this model (658 > 512). Running this sequence through the model will result in indexing errors
Average accuracy 0.414 - astronomy
Average accuracy 0.500 - business_ethics
Average accuracy 0.464 - clinical_knowledge
Average accuracy 0.451 - college_biology
Average accuracy 0.320 - college_chemistry
Average accuracy 0.340 - college_computer_science
Average accuracy 0.360 - college_mathematics
Average accuracy 0.445 - college_medicine
Average accuracy 0.186 - college_physics
Average accuracy 0.550 - computer_security
Average accuracy 0.409 - conceptual_physics
Average accuracy 0.289 - econometrics
Average accuracy 0.441 - electrical_engineering
Average accuracy 0.286 - elementary_mathematics
Average accuracy 0.310 - formal_logic
Average accuracy 0.320 - global_facts
Average accuracy 0.484 - high_school_biology
Average accuracy 0.330 - high_school_chemistry
Average accuracy 0.420 - high_school_computer_science
Average accuracy 0.600 - high_school_european_history
Average accuracy 0.530 - high_school_geography
Average accuracy 0.674 - high_school_government_and_politics
Average accuracy 0.449 - high_school_macroeconomics
Average accuracy 0.289 - high_school_mathematics
Average accuracy 0.420 - high_school_microeconomics
Average accuracy 0.325 - high_school_physics
Average accuracy 0.631 - high_school_psychology
Average accuracy 0.287 - high_school_statistics
Average accuracy 0.583 - high_school_us_history
Average accuracy 0.637 - high_school_world_history
Average accuracy 0.574 - human_aging
Average accuracy 0.565 - human_sexuality
Average accuracy 0.636 - international_law
Average accuracy 0.509 - jurisprudence
Average accuracy 0.564 - logical_fallacies
Average accuracy 0.384 - machine_learning
Average accuracy 0.553 - management
Average accuracy 0.701 - marketing
Average accuracy 0.540 - medical_genetics
Average accuracy 0.641 - miscellaneous
Average accuracy 0.512 - moral_disputes
Average accuracy 0.238 - moral_scenarios
Average accuracy 0.500 - nutrition
Average accuracy 0.582 - philosophy
Average accuracy 0.503 - prehistory
Average accuracy 0.358 - professional_accounting
Average accuracy 0.351 - professional_law
Average accuracy 0.540 - professional_medicine
Average accuracy 0.433 - professional_psychology
Average accuracy 0.536 - public_relations
Average accuracy 0.429 - security_studies
Average accuracy 0.602 - sociology
Average accuracy 0.650 - us_foreign_policy
Average accuracy 0.428 - virology
Average accuracy 0.690 - world_religions
Average accuracy 0.297 - math
Average accuracy 0.496 - health
Average accuracy 0.355 - physics
Average accuracy 0.620 - business
Average accuracy 0.474 - biology
Average accuracy 0.327 - chemistry
Average accuracy 0.422 - computer science
Average accuracy 0.415 - economics
Average accuracy 0.441 - engineering
Average accuracy 0.408 - philosophy
Average accuracy 0.545 - other
Average accuracy 0.572 - history
Average accuracy 0.530 - geography
Average accuracy 0.554 - politics
Average accuracy 0.526 - psychology
Average accuracy 0.587 - culture
Average accuracy 0.380 - law
Average accuracy 0.363 - STEM
Average accuracy 0.430 - humanities
Average accuracy 0.512 - social sciences
Average accuracy 0.530 - other (business, health, misc.)
Average accuracy: 0.457
```

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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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).

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

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.14.0