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README.md

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+---
+license: apache-2.0
+datasets:
+- fblgit/tree-of-knowledge
+- Open-Orca/SlimOrca-Dedup
+- allenai/ultrafeedback_binarized_cleaned
+library_name: transformers
+tags:
+- juanako
+- UNA
+- cybertron
+- fbl
+---
+
+# Model Card for una-cybertron-7b-v2-bf16 (UNA: Uniform Neural Alignment)
+
+We strike back, introducing **Cybertron 7B v2** a 7B MistralAI based model, best on it's series. Trained on SFT, DPO and UNA (Unified Neural Alignment) on multiple datasets.
+He scores [EXACTLY](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__una-cybertron-7b-v2-bf16)  **#1** with **69.67**+ score on HF LeaderBoard board, **#8** ALL SIZES top score.
+
+* v1 Scoring **#1** at 2 December 2023 with 69.43 ..few models were releasse .. but only 1 can survive: CYBERRTON!
+* v2 Scoring **#1** at 5 December 2023 with 69.67
+2
+  
+| Model | Average | ARC (25-s) | HellaSwag (10-s) | MMLU (5-s) | TruthfulQA (MC) (0-s) | Winogrande (5-s) | GSM8K (5-s) |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 60.97 | 59.98  | 83.31  | 64.16  | 42.15 | 78.37 | 37.83 |
+| [Intel/neural-chat-7b-v3-2](https://huggingface.co/Intel/neural-chat-7b-v3-2) | 68.29 | 67.49  | 83.92  | 63.55  | 59.68 | 79.95 | 55.12 |
+| [perlthoughts/Chupacabra-7B-v2](https://huggingface.co/perlthoughts/Chupacabra-7B-v2) | 63.54 | 66.47 | 85.17 | 64.49  | 57.6 | 79.16 | 28.35 |
+| [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** |
+
+The model excels in mathematics, logic, reasoning, overall very smart.
+
+## 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.](xavi@juanako.ai)
+- **Investors** [CONTACT HERE](billing@juanako.ai)
+- **Model type:** MistralAI 7B
+- **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 4.35.0-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{unacybertron7b,
+  title={Cybertron: Uniform Neural Alignment}, 
+  author={Xavier Murias},
+  year={2023},
+  publisher = {HuggingFace},
+  journal = {HuggingFace repository},
+  howpublished = {\url{https://huggingface.co/fblgit/una-cybertron-7b-v2-bf16}},
+}
+```
+
+Special thanks to @TheBloke & @bartowski for converting the models and their support to the community. Thank you!

config.json

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+{
+  "_name_or_path": "fblgit/juanako-7b-UNA",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.35.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.35.2"
+}

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