fblgit
/

una-xaberius-34b-v1beta

@@ -1,202 +1,99 @@
 ---
 ---
-# una-xaberius-34b-v1-beta (UNA: Uniform Neural Alignment)
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Data 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. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Data Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-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).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]
-## Training procedure
 ### Framework versions
-- PEFT 0.6.1

 ---
+license: cc-by-4.0
+datasets:
+- allenai/ultrafeedback_binarized_cleaned
+- fblgit/tree-of-knowledge
+- garage-bAInd/Open-Platypus
+- Open-Orca/OpenOrca
+library_name: transformers
+tags:
+- UNA
+- juanako
+- cybertron
+- xaberius
 ---
+# Model Card for una-xaberius-34b-v1-beta (UNA: Uniform Neural Alignment)
+Introducing THE MODEL: **XABERIUS 34B v1-BETA** an *experimental* 34B LLaMa-Yi-34B based model, best on it's series. Trained on SFT, DPO and UNA (Unified Neural Alignment) on multiple datasets.
+Timeline:
+* 05-Dec-2023 **v1-beta released**
+*
+| Model | Average | ARC (25-s) | HellaSwag (10-s) | MMLU (5-s) | TruthfulQA (MC) (0-s) | Winogrande (5-s) | GSM8K (5-s) |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| [fblgit/una-cybertron-7b-v1-fp16](https://huggingface.co/fblgit/una-cybertron-7b-v1-fp16) | **69.49** | **68.43** | **85.85** | 63.34  | **63.28** | **80.90** | **55.12** |
+| [fblgit/una-cybertron-7b-v2-bf16](https://huggingface.co/fblgit/una-cybertron-7b-v2-bf16) | **69.67** | **68.26** | **85.?4** | 63.23  | **64.63** | **81.37** | **55.04** |
+.. xaberius results will come out soon.
+## Model Details
+Adiestrated with UNA: Uniform Neural Alignment technique (paper going out soon).
+* What is **NOT** UNA? Its not a merged layers model. Is not SLERP or SLURP or similar.
+* What **is** UNA? A formula & A technique to *TAME* models
+* When will be released the code and paper? When have time, contribute and it'll be faster.
+### Model Description
+- **Developed by:** [juanako.ai](https://juanako.ai)
+- **Author:** [Xavier M.]([email protected])
+- **Investors** [CONTACT HERE]([email protected])
+- **Model type:** LLaMa YI-34B
+- **Funded by Cybertron's H100's** with few hours training.
+### Prompt
+The model is very good, works well on almost any prompt but ChatML format and Alpaca System gets the best
+```
+<|im_start|>system
+- You are a helpful assistant chatbot trained by MosaicML.
+- You answer questions.
+- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
+- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>
+<|im_start|>user
+Explain QKV<|im_end|>
+<|im_start|>assistant
+```
+```
+### Assistant: I am StableVicuna, a large language model created by CarperAI. I am here to chat!
+### Human: Explain QKV
+### Assistant:
+```
+```
+[Round <|round|>]
+问：Explain QKV
+答：
+```
+```
+[Round <|round|>]
+Question：Explain QKV
+Answer：
+```
+```
+Question：Explain QKV
+Answer：
+```
 ### Framework versions
+- Transformers 4.35.2-UNA
+- Pytorch 2.1.0
+- Datasets 2.14.6
+- Tokenizers 0.14.1
+### Citations
+    If you find Cybertron, Juanako or any of our models useful, specially if you use it for your big brand.. or you clone/merge my modelsm, cite please:
+```
+@misc{unaxaberius34b,
+  title={Xaberius 34B: Uniform Neural Alignment},
+  author={Xavier Murias},
+  year={2023},
+  publisher = {HuggingFace},
+  journal = {HuggingFace repository},
+  howpublished = {\url{https://huggingface.co/fblgit/una-xaberius-34b-v1beta}},
+}
+```
+Special thanks to @TheBloke & @bartowski for converting the models and their support to the community. Thank you!