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base_model: model
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library_name: peft
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pipeline_tag: text-generation
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tags:
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- axolotl
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- base_model:adapter:model
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- lora
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- transformers
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---
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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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[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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#### 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 Contact
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[More Information Needed]
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### Framework versions
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- PEFT 0.17.0
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---
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library_name: peft
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tags:
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- axolotl
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- base_model:adapter:model
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- lora
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- transformers
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datasets:
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- PocketDoc/Dans-Prosemaxx-RepRemover-1
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- PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2
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- Delta-Vector/Orion-Personamaxx-RP-Deslopped-V1
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- Delta-Vector/Orion-PIPPA-Cleaned-V2
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- Delta-Vector/Orion-Creative_Writing-Complexity
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- Delta-Vector/Orion-OpenCAI-ShareGPT
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- GreenerPastures/AllYourBase5k
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pipeline_tag: text-generation
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base_model: model
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model-index:
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- name: MS32-3
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.12.0.dev0`
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```yaml
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base_model: model
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hub_model_id: hardlyworking/MS32-3
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hub_strategy: "all_checkpoints"
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push_dataset_to_hub:
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hf_use_auth_token: true
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plugins:
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- axolotl.integrations.liger.LigerPlugin
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- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
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liger_rope: true
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liger_rms_norm: true
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liger_layer_norm: true
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liger_glu_activation: true
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liger_fused_linear_cross_entropy: false
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cut_cross_entropy: true
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load_in_8bit: false
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load_in_4bit: true
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chat_template: mistral_v7_tekken
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datasets:
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- path: PocketDoc/Dans-Prosemaxx-RepRemover-1
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type: chat_template
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split: train
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field_messages: conversations
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message_property_mappings:
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role: from
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content: value
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user: human
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assistant: gpt
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- path: PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2
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type: chat_template
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split: train
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field_messages: conversations
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message_property_mappings:
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role: from
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content: value
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user: human
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assistant: gpt
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- path: Delta-Vector/Orion-Personamaxx-RP-Deslopped-V1
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type: chat_template
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split: train
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field_messages: conversations
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message_property_mappings:
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role: from
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content: value
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user: human
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assistant: gpt
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- path: Delta-Vector/Orion-PIPPA-Cleaned-V2
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type: chat_template
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split: train
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field_messages: conversations
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message_property_mappings:
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role: from
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content: value
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user: human
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assistant: gpt
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- path: Delta-Vector/Orion-Creative_Writing-Complexity
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type: chat_template
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split: train
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field_messages: conversations
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message_property_mappings:
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role: from
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content: value
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user: human
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assistant: gpt
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- path: Delta-Vector/Orion-OpenCAI-ShareGPT
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type: chat_template
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split: train
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field_messages: conversations
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message_property_mappings:
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role: from
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content: value
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user: human
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assistant: gpt
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- path: GreenerPastures/AllYourBase5k
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type: chat_template
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split: train
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field_messages: conversations
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message_property_mappings:
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role: from
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content: value
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user: human
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assistant: gpt
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val_set_size: 0.0
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output_dir: ./outputs/out
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adapter: qlora
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lora_r: 64
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lora_alpha: 32
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lora_dropout: 0.0
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lora_target_linear: true
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peft_use_rslora: true
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sequence_len: 8192
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sample_packing: true
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eval_sample_packing: true
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pad_to_sequence_len: true
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wandb_project: MS32-3
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wandb_entity:
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wandb_watch:
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wandb_name: MS32-3
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wandb_log_model:
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gradient_accumulation_steps: 32
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micro_batch_size: 1
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num_epochs: 1
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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+
learning_rate: 4e-5
|
145 |
+
max_grad_norm: 1.0
|
146 |
+
|
147 |
+
bf16: auto
|
148 |
+
tf32: true
|
149 |
+
|
150 |
+
gradient_checkpointing: true
|
151 |
+
gradient_checkpointing_kwargs:
|
152 |
+
use_reentrant: false
|
153 |
+
unsloth: true
|
154 |
+
resume_from_checkpoint:
|
155 |
+
logging_steps: 1
|
156 |
+
flash_attention: true
|
157 |
+
|
158 |
+
warmup_ratio: 0.1
|
159 |
+
evals_per_epoch:
|
160 |
+
saves_per_epoch: 4
|
161 |
+
weight_decay: 0.0025
|
162 |
+
special_tokens:
|
163 |
+
|
164 |
+
```
|
165 |
+
|
166 |
+
</details><br>
|
167 |
+
|
168 |
+
# MS32-3
|
169 |
+
|
170 |
+
This model was trained from scratch on the PocketDoc/Dans-Prosemaxx-RepRemover-1, the PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2, the Delta-Vector/Orion-Personamaxx-RP-Deslopped-V1, the Delta-Vector/Orion-PIPPA-Cleaned-V2, the Delta-Vector/Orion-Creative_Writing-Complexity, the Delta-Vector/Orion-OpenCAI-ShareGPT and the GreenerPastures/AllYourBase5k datasets.
|
171 |
+
|
172 |
+
## Model description
|
173 |
+
|
174 |
+
More information needed
|
175 |
+
|
176 |
+
## Intended uses & limitations
|
177 |
+
|
178 |
+
More information needed
|
179 |
+
|
180 |
+
## Training and evaluation data
|
181 |
+
|
182 |
+
More information needed
|
183 |
+
|
184 |
+
## Training procedure
|
185 |
+
|
186 |
+
### Training hyperparameters
|
187 |
+
|
188 |
+
The following hyperparameters were used during training:
|
189 |
+
- learning_rate: 4e-05
|
190 |
+
- train_batch_size: 1
|
191 |
+
- eval_batch_size: 1
|
192 |
+
- seed: 42
|
193 |
+
- gradient_accumulation_steps: 32
|
194 |
+
- total_train_batch_size: 32
|
195 |
+
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
|
196 |
+
- lr_scheduler_type: cosine
|
197 |
+
- lr_scheduler_warmup_steps: 10
|
198 |
+
- training_steps: 100
|
199 |
|
200 |
+
### Training results
|
201 |
|
|
|
202 |
|
|
|
203 |
|
|
|
204 |
### Framework versions
|
205 |
|
206 |
+
- PEFT 0.17.0
|
207 |
+
- Transformers 4.55.0
|
208 |
+
- Pytorch 2.7.1+cu126
|
209 |
+
- Datasets 4.0.0
|
210 |
+
- Tokenizers 0.21.4
|