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Delete Prompt_Inverter

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  1. Prompt_Inverter/.ipynb_checkpoints/trainer_log-checkpoint.jsonl +0 -0
  2. Prompt_Inverter/.ipynb_checkpoints/training_loss-checkpoint.png +0 -0
  3. Prompt_Inverter/adapter_config.json +0 -31
  4. Prompt_Inverter/adapter_model.safetensors +0 -3
  5. Prompt_Inverter/added_tokens.json +0 -13
  6. Prompt_Inverter/all_results.json +0 -8
  7. Prompt_Inverter/checkpoint-12000/README.md +0 -202
  8. Prompt_Inverter/checkpoint-12000/adapter_config.json +0 -31
  9. Prompt_Inverter/checkpoint-12000/adapter_model.safetensors +0 -3
  10. Prompt_Inverter/checkpoint-12000/added_tokens.json +0 -13
  11. Prompt_Inverter/checkpoint-12000/optimizer.pt +0 -3
  12. Prompt_Inverter/checkpoint-12000/rng_state_0.pth +0 -3
  13. Prompt_Inverter/checkpoint-12000/rng_state_1.pth +0 -3
  14. Prompt_Inverter/checkpoint-12000/rng_state_2.pth +0 -3
  15. Prompt_Inverter/checkpoint-12000/rng_state_3.pth +0 -3
  16. Prompt_Inverter/checkpoint-12000/rng_state_4.pth +0 -3
  17. Prompt_Inverter/checkpoint-12000/rng_state_5.pth +0 -3
  18. Prompt_Inverter/checkpoint-12000/scheduler.pt +0 -3
  19. Prompt_Inverter/checkpoint-12000/special_tokens_map.json +0 -30
  20. Prompt_Inverter/checkpoint-12000/tokenizer.json +0 -0
  21. Prompt_Inverter/checkpoint-12000/tokenizer.model +0 -3
  22. Prompt_Inverter/checkpoint-12000/tokenizer_config.json +0 -132
  23. Prompt_Inverter/checkpoint-12000/trainer_state.json +0 -0
  24. Prompt_Inverter/checkpoint-12000/training_args.bin +0 -3
  25. Prompt_Inverter/checkpoint-13041/README.md +0 -202
  26. Prompt_Inverter/checkpoint-13041/adapter_config.json +0 -31
  27. Prompt_Inverter/checkpoint-13041/adapter_model.safetensors +0 -3
  28. Prompt_Inverter/checkpoint-13041/added_tokens.json +0 -13
  29. Prompt_Inverter/checkpoint-13041/optimizer.pt +0 -3
  30. Prompt_Inverter/checkpoint-13041/rng_state_0.pth +0 -3
  31. Prompt_Inverter/checkpoint-13041/rng_state_1.pth +0 -3
  32. Prompt_Inverter/checkpoint-13041/rng_state_2.pth +0 -3
  33. Prompt_Inverter/checkpoint-13041/rng_state_3.pth +0 -3
  34. Prompt_Inverter/checkpoint-13041/rng_state_4.pth +0 -3
  35. Prompt_Inverter/checkpoint-13041/rng_state_5.pth +0 -3
  36. Prompt_Inverter/checkpoint-13041/scheduler.pt +0 -3
  37. Prompt_Inverter/checkpoint-13041/special_tokens_map.json +0 -30
  38. Prompt_Inverter/checkpoint-13041/tokenizer.json +0 -0
  39. Prompt_Inverter/checkpoint-13041/tokenizer.model +0 -3
  40. Prompt_Inverter/checkpoint-13041/tokenizer_config.json +0 -132
  41. Prompt_Inverter/checkpoint-13041/trainer_state.json +0 -0
  42. Prompt_Inverter/checkpoint-13041/training_args.bin +0 -3
  43. Prompt_Inverter/checkpoint-3000/README.md +0 -202
  44. Prompt_Inverter/checkpoint-3000/adapter_config.json +0 -31
  45. Prompt_Inverter/checkpoint-3000/adapter_model.safetensors +0 -3
  46. Prompt_Inverter/checkpoint-3000/added_tokens.json +0 -13
  47. Prompt_Inverter/checkpoint-3000/optimizer.pt +0 -3
  48. Prompt_Inverter/checkpoint-3000/rng_state_0.pth +0 -3
  49. Prompt_Inverter/checkpoint-3000/rng_state_1.pth +0 -3
  50. Prompt_Inverter/checkpoint-3000/rng_state_2.pth +0 -3
Prompt_Inverter/.ipynb_checkpoints/trainer_log-checkpoint.jsonl DELETED
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Prompt_Inverter/.ipynb_checkpoints/training_loss-checkpoint.png DELETED
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- ---
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- base_model: /root/autodl-tmp/LLM-Research/Phi-3-medium-128k-instruct
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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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- - **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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- - **Repository:** [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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- #### 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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- ## 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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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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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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- ### 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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- ## 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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- ## Glossary [optional]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ### Framework versions
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- - PEFT 0.12.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- - PEFT 0.12.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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