full icliniq dataset finetune (#1)
Browse files- full icliniq dataset finetune (a88e5ee09d3661b68808b35bd5d4ea9cee480b2a)
Co-authored-by: Dev <[email protected]>
- README.md +184 -39
- adapter_config.json +3 -3
- adapter_model.safetensors +1 -1
- optimizer.pt +3 -0
- rng_state.pth +3 -0
- scheduler.pt +3 -0
- trainer_state.json +1202 -0
- training_args.bin +2 -2
README.md
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---
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base_model: google/gemma-2b
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library_name:
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model_name: gemma2b-ft-iclinq
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tags:
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- generated_from_trainer
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- trl
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- sft
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licence: license
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---
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# Model Card for
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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generator = pipeline("text-generation", model="jash6/results-2", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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This model was trained with SFT.
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- Transformers: 4.46.3
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- Pytorch: 2.2.0
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- Datasets: 3.1.0
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- Tokenizers: 0.20.4
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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---
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base_model: google/gemma-2b
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library_name: peft
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---
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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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### Model Description
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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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[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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- PEFT 0.13.2
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adapter_config.json
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"gate_proj",
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"v_proj",
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"up_proj",
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"k_proj",
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"q_proj",
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"o_proj"
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"task_type": "CAUSAL_LM",
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"v_proj",
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"q_proj",
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"k_proj",
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"gate_proj",
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"up_proj",
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"o_proj"
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"task_type": "CAUSAL_LM",
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 14331480
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version https://git-lfs.github.com/spec/v1
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oid sha256:05ec9512d068cc5c7f2376de462eaf86f3678a7ef989d959fd464ad562ced5bd
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size 14331480
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optimizer.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:65481820a18e53afd9b92fa8ed1ef317a6b48ce02a409c7a018ae0552b46ac81
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size 28734458
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rng_state.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:b8ff545097dd45f4e0976ceb12627a2db85a8cda9404b0b67df2f4c97490e22c
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size 14244
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scheduler.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:fa275e0c3e75f7e8222fcb1c9374e42a40ab63cd9b97d2e82dc002aa81170f4b
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size 1064
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trainer_state.json
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