Update README.md

README.md

@@ -1,13 +1,16 @@
 ---
 base_model: meta-llama/Llama-2-7b-hf
 library_name: peft
+license: apache-2.0
+language:
+- en
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Info
 
+This is a model that applies LLM2Vec to Llama-2. Only the PEFT Adapter is distributed.
+LLM2Vec is fine-tuned on two tasks: MNTP and SimCSE, and this repository contains the results of applying SimCSE after MNTP. 
+For the MNTP Adapter, please refer to [this link](https://huggingface.co/uzabase/LLM2Vec-Llama-2-7b-hf-wikipedia-jp-mntp).
 
 ## Model Details
 
@@ -15,188 +18,92 @@ library_name: peft
 
 <!-- Provide a longer summary of what this model is. -->
 
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Model type:** PEFT
+- **Language(s) (NLP):** English
+- **License:** Apache2.0
+- **Finetuned from model:** [Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-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.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### 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 -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### 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]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
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-[More Information Needed]
-
-**APA:**
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-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- 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]
-
-## 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]
-
-## Model Card Contact
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-[More Information Needed]
-### Framework versions
-
-- PEFT 0.11.1
+- **Repository:**  https://github.com/McGill-NLP/llm2vec
+- **Paper:** https://arxiv.org/abs/2404.05961
+
+# Usage
+
+- Please see [original LLM2Vec repo](https://huggingface.co/McGill-NLP/LLM2Vec-Llama-2-7b-chat-hf-mntp-unsup-simcse#usage)
+
+# BenchMark
+- Followings are summaries. Details are [here]()
+## MTEB(Japansese)
+
+| | Classification | Clustering | PairClassification | Reranking | BitextMining | Retrieval | Sts | AVG |
+| --- | ---| ---| ---| ---| ---|  ---| ---| ---| ---| ---| ---| ---| ---| ---|
+| |Llama2-Llm2vec-eng (This repo) | 0.527 | 0.258 | 0.501 | 0.217 | 0.275 | 0.296 | 0.765 | 0.408 |
+| Llama2-Llm2vec-jpn | 0.570 | 0.365 | 0.510 | 0.349 | 0.470 | 0.417 | 0.795 | 0.498 |
+| Swallow-Llm2vec-jpn | 0.621 | 0.391 | 0.510 | 0.475 | 0.475 | 0.491 | 0.832 | 0.523 |
+
+## MTEB(English)
+
+
+# Training Details
+
+## Training Data
+
+- [Corpus for SimCSE from Wikipedia](https://github.com/McGill-NLP/llm2vec?tab=readme-ov-file#unsupervised-contrastive-training-simcse)
+
+
+## Training Hyperparameter
+- simcse_dropout: 0.3
+- bidirectional: true
+- pooling_mode: "mean"
+- remove_unused_columns: false
+- learning_rate: 3e-5
+- loss_scale: 20
+- batch_size: 256
+- gradient_accumulation_steps: 1
+- max_seq_length: 128
+- lora_r: 16
+- torch_dtype: "bfloat16"
+- attn_implementation: "flash_attention_2"
+- seed: 42
+- bf16: true
+- gradient_checkpointing: true
+    
+
+## Accelerator Settings
+- deepspeed_config:
+  - gradient_accumulation_steps: 1
+  - gradient_clipping: 1.0
+  - offload_optimizer_device: nvme
+  - offload_optimizer_nvme_path: /nvme
+  - zero3_save_16bit_model: true
+  - zero_stage: 2 
+- distributed_type: DEEPSPEED
+- downcast_bf16: 'no'
+- dynamo_config:
+  - dynamo_backend: INDUCTOR
+  - dynamo_mode: default
+  - dynamo_use_dynamic: true
+  - dynamo_use_fullgraph: true
+- enable_cpu_affinity: false
+- machine_rank: 0
+- main_training_function: main
+- mixed_precision: bf16
+- num_machines: 1
+- num_processes: 2
+- rdzv_backend: static
+- same_network: true
+- quse_cpu: false
+
+
+## Framework versions
+
+- Python: 3.12.3
+- PEFT 0.11.1
+- Sentence Transformers: 3.0.1
+- Transformers: 4.41.0
+- PyTorch: 2.3.0
+- Accelerate: 0.30.1
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+- MTEB: 1.13.0