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@@ -9,35 +9,67 @@ pipeline_tag: text-generation
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  ---
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  # Model Card for neoncortex/mini-mistral-openhermes-2.5-chatml-test
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- A tiny Mistral model trained on teknium/OpenHermes-2.5. This is epoch 3/9, so it's early in training.
 
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  ## Model Details
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- ### Model Description
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-
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- <!-- Provide a longer summary of what this model is. -->
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-
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-
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-
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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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-
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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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  ### 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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  <!-- 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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-
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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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  ## 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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-
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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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-
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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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  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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- [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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  ---
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  # Model Card for neoncortex/mini-mistral-openhermes-2.5-chatml-test
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+ A tiny Mistral model trained on teknium/OpenHermes-2.5.
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+ This is epoch 5/9, so still some training to go.
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  ## Model Details
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+ A 63M parameter auto-regressive LM using Mistral architecture as a base.
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+ - Multi-query Attention instead of Grouped-query Attention.
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+ - Sliding window is disabled.
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+ - Modified ChatML instead of Mistral chat template - TL;DR I used '<|im_start|>human' instead of '<|im_start|>user'
 
 
 
 
 
 
 
 
 
 
 
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+ ### Model Description
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+ Just doing it to see what happens.
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+ It'll take about 40 to 45 hours to train on two Nvidia RTX 3060 12GB.
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+ It uses ChatML for the chat template, but I fucked up the template in the dataset,
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+ using '<|im_start|>human' instead of '<|im_start|>user'. ¯\_(ツ)_/¯
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+ So, here's the bits:
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+ ```
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+ {%- set ns = namespace(found=false) -%}
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+ {%- for message in messages -%}
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+ {%- if message['role'] == 'system' -%}
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+ {%- set ns.found = true -%}
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+ {%- endif -%}
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+ {%- endfor -%}
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+ {%- for message in messages %}
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+ {%- if message['role'] == 'system' -%}
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+ {{- '<|im_start|>system\n' + message['content'].rstrip() + '<|im_end|>\n' -}}
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+ {%- else -%}
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+ {%- if message['role'] == 'user' -%}
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+ {{-'<|im_start|>human\n' + message['content'].rstrip() + '<|im_end|>\n'-}}
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+ {%- else -%}
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+ {{-'<|im_start|>assistant\n' + message['content'] + '<|im_end|>\n' -}}
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+ {%- endif -%}
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+ {%- endif -%}
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+ {%- endfor -%}
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+ {%- if add_generation_prompt -%}
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+ {{-'<|im_start|>assistant\n'-}}
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+ {%- endif -%}
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+ ```
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+
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+ - **Developed by:** gronkomatic
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+ - **Funded by:** gronkomatic
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+ - **Shared by:** gronkomatic
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+ - **Model type:** Mistral
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+ - **Language(s) (NLP):** English, maybe others I dunno
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+ - **License:** OpenRAIL, IDGAF
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+ ### Model Sources
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+ Exclusively available right here on HuggingFace!
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+ - **Repository:** https://huggingface.co/neoncortex/mini-mistral-openhermes-2.5-chatml-test
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+ - **Paper:** LoL
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+ - **Demo:** Just download it in Oobabooga and use the modified chatML template above. Maybe I'll throw together a Space or something.
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  ## Uses
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+ If you wanna have a laugh at how bad it is then go ahead, but I wouldn't expect much from it.
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  ### Direct Use
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  ### Out-of-Scope Use
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+ This model won't work well for pretty much everything, probably.
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  [More Information Needed]
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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
 
 
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+ I took the OpenHermes 2.5 dataset formatted it with ChatML.
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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
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+ epochs: 9
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+ steps: 140976
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+ batches per device: 6
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+ 1.04it/s
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  <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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  ## Evaluation
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+ I tried to run evals but the eval suite just laughed at me.
 
 
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+ ## Model Examination
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+ Don't be rude.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Environmental Impact
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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:** I already told you. Try and keep up.
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+ - **Hours used:** ~45 x 2 I guess.
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+ - **Cloud Provider:** gronkomatic
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+ - **Compute Region:** myob
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+ - **Carbon Emitted:** Probably
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+ ## Technical Specifications
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  ### Model Architecture and Objective
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  ### Compute Infrastructure
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+ I trained it on my PC with no side on it because I like to watch the GPUs do their work.
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  #### Hardware
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+ 2 x Nvidia RTX 3060 12GB
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  #### Software
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+ The wonderful free stuff at HuggingFace (https://huggingface.co)[https://huggingface.co]: transformers, datasets, trl
 
 
 
 
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+ ## Glossary
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+ IDGAF - I don't give a fuck
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+ ## More Information
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  [More Information Needed]
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+ ## Model Card Authors
 
 
 
 
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+ gronkomatic, unless you're offended by something, in which case it was hacked by hackers.
 
 
 
 
 
 
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  ## Model Card Contact
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+ If you want to send me insults come find me on Reddit I guess u/gronkomatic.