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library_name: peft
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# Model
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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<!-- Provide a longer summary of what this model is. -->
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- **
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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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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## 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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[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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### Framework versions
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---
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base_model:
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- meta-llama/Llama-2-7b-hf
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library_name: peft
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license: apache-2.0
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datasets:
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- wikimedia/wikipedia
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language:
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- ja
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- en
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# Model Info
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This is a model that applies LLM2Vec to Swallow. Only the PEFT Adapter is distributed. LLM2Vec fine-tunes on two tasks: MNTP and SimCSE, but this repository contains the results of applying only the MNTP task.
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## Model Details
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<!-- Provide a longer summary of what this model is. -->
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- **Model type:** PEFT
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- **Language(s) (NLP):** Japanese
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- **License:** Apache2.0
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- **Finetuned from model [optional]:** [llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
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### Model Sources [optional]
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- **Repository:** https://github.com/McGill-NLP/llm2vec
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- **Paper:** https://arxiv.org/abs/2404.05961
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## Usage
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- Please see [original LLM2Vec repo](https://huggingface.co/McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp#usage)
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## Training Details
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### Training Data
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- [Wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia)
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#### Training Hyperparameter
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- batch_size: 64,
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- gradient_accumulation_steps: 1
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- max_seq_length": 512,
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- mask_token_type: "blank"
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- mlm_probability: 0.2
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- lora_r: 16
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- torch_dtype "bfloat16"
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- attn_implementation "flash_attention_2"
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- bf16: true
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- gradient_checkpointing: true
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#### Accelerator Settings
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- deepspeed_config:
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- gradient_accumulation_steps: 1
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- gradient_clipping: 1.0
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- offload_optimizer_device: nvme
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- offload_optimizer_nvme_path: /nvme
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- zero3_save_16bit_model: true
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- zero_stage: 2
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- distributed_type: DEEPSPEED
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- downcast_bf16: 'no'
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- dynamo_config:
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- dynamo_backend: INDUCTOR
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- dynamo_mode: default
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- dynamo_use_dynamic: true
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- dynamo_use_fullgraph: true
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- enable_cpu_affinity: false
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- machine_rank: 0
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- main_training_function: main
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- mixed_precision: bf16
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- num_machines: 1
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- num_processes: 2
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- rdzv_backend: static
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- same_network: true
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- quse_cpu: false
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### Framework versions
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- Python: 3.12.3
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- PEFT 0.11.1
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- Sentence Transformers: 3.0.1
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- Transformers: 4.41.0
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- PyTorch: 2.3.0
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- Accelerate: 0.30.1
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- Datasets: 2.20.0
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- Tokenizers: 0.19.1
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- MTEB: 1.13.0
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