PEFT
Safetensors
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  base_model: meta-llama/Llama-2-7b-hf
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  library_name: peft
 
 
 
 
 
 
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- # Model Card for Model ID
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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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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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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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- <!-- Provide the basic links for the model. -->
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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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- <!-- 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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- #### 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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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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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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- **APA:**
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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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- ## Model Card Authors [optional]
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- ## Model Card Contact
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  ### Framework versions
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- - PEFT 0.11.1
 
 
 
 
 
 
 
 
 
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  base_model: 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:** [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