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library_name: transformers
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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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[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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**
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##
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language:
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- ko
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- en
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pipeline_tag: text-generation
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inference: false
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tags:
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- solar
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- mistral
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- pytorch
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- solar-ko
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library_name: transformers
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license: apache-2.0
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**Update Log**
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- 2024.02.19: Initial Test version Release of SOLAR-KOEN-10.8B
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# **Solar-Ko-Recovery** ⭐🇰🇷🇺🇸
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Solar-Ko-Recovery aimed to recover Solar's capability on Korean with re-arrange of Embeddings and LM head, featuring an expanded vocabulary and the inclusion of a Korean+English corpus for enhanced representation.
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## Model Details
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**Model Developers:** Junbum Lee (Beomi)
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**Variations:** Solar-Ko-Recovery is available with one parameter sizes — 10.8B.
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**Input:** The model accepts only text input.
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**Output:** The model produces text output exclusively.
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**Model Architecture:**
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Solar-Ko-Recovery is an auto-regressive language model that leverages an optimized transformer architecture derived from Llama-2.
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| |Training Data|Parameters|Content Length|GQA|Tokens|Learning Rate|
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|Solar-Ko-Recovery|*A curated mix of Korean+English Corpora*|10.8B|4k|O|>30B*|5e<sup>-5</sup>|
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**Vocab Expansion**
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Vocab expansion is conducted on edited [upstage/solar-1-mini-tokenizer](https://huggingface.co/upstage/solar-1-mini-tokenizer), which is superset of Solar tokenizer.
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| Model Name | Vocabulary Size | Description |
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| --- | --- | --- |
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| Original Solar | 32000 | Sentencepiece BPE |
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| **solar-1-mini-tokenizer** | 64000 | Sentencepiece BPE. Added Ko/JP vocabs |
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**Tokenizing "안녕하세요, 오늘은 날씨가 좋네요."**
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- SOLAR-10.7B: 26 tokens
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- SOLAR-KO-10.7b: 7 tokens
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| Model | Tokens |
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| --- | --- |
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| SOLAR-10.7B | `['▁', '안', '<0xEB>', '<0x85>', '<0x95>', '하', '세', '요', ',', '▁', '오', '<0xEB>', '<0x8A>', '<0x98>', '은', '▁', '날', '<0xEC>', '<0x94>', '<0xA8>', '가', '▁', '좋', '네', '요', '.']` |
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| Solar-Ko-Recovery | `['▁안녕하세요', ',', '▁오늘은', '▁날씨가', '▁좋', '네요', '.']` |
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**Tokenizing "Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!"**
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- SOLAR-10.7B: 22 tokens
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- SOLAR-KO-10.7b: 22 tokens
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| Model | Tokens |
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| --- | --- |
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| SOLAR-10.7B | `['▁Meet', '▁', '1', '0', '.', '7', 'B', '▁Solar', ':', '▁E', 'lev', 'ating', '▁Performance', '▁with', '▁Up', 'stage', '▁Dep', 'th', '▁UP', '▁Scal', 'ing', '!']` |
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| Solar-Ko-Recovery | `['▁Meet', '▁', '1', '0', '.', '7', 'B', '▁Solar', ':', '▁E', 'lev', 'ating', '▁Performance', '▁with', '▁Up', 'stage', '▁Dep', 'th', '▁UP', '▁Scal', 'ing', '!']` |
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# LICENSE
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Apache 2.0
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# **Model Benchmark**
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## LM Eval Harness - Korean
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- Used EleutherAI's [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
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- 5-shot scores
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TBD
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## Citation
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TBD
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## Acknowledgements
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- Training support was provided by the [TPU Research Cloud](https://sites.research.google/trc/) program.
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