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Model: aixonlab/Grey-12b
Source: Original Platform

README.md

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+---
+base_model: aixonlab/Aether-12b
+language:
+- en
+license: apache-2.0
+tags:
+- text-generation-inference
+- transformers
+- mistral
+---
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/66dcee3321f901b049f48002/jWXtbknuetFdz5fkFn-ey.png" width="800"/>
+
+# Grey-12b
+
+Grey-12b is a merged language model created by combining multiple models using the della_linear merge method, with Aether-12b as the base model.
+
+## Model Details 📊
+- Developed by: AIXON Lab
+- Model type: Merged Causal Language Model
+- Language(s): English (primarily), may support other languages
+- License: apache-2.0
+- Repository: https://huggingface.co/aixonlab/Grey-12b
+
+## Model Architecture 🏗️
+- Base model: aixonlab/Aether-12b
+- Parameter count: ~12 billion
+- Architecture specifics: Transformer-based language model
+- Merge method: della_linear
+
+### Merged Models
+1. VAGOsolutions/SauerkrautLM-Nemo-12b-Instruct
+   - Weight: 0.33
+   - Density: 0.4
+2. cognitivecomputations/dolphin-2.9.3-mistral-nemo-12b
+   - Weight: 0.77
+   - Density: 0.8
+
+## Technical Specifications
+- Dtype: float16
+- Tokenizer source: base (aixonlab/Aether-12b)
+- Merge parameters:
+  - Epsilon: 0.05
+  - Lambda: 1
+
+## Intended Use 🎯
+As an advanced language model for various natural language processing tasks, including but not limited to text generation, question-answering, and analysis.
+
+## Ethical Considerations 🤔
+As a merged model based on multiple sources, Grey-12b may inherit biases and limitations from its constituent models. Users should be aware of potential biases in generated content and use the model responsibly.
+
+## Performance and Evaluation
+Performance metrics and evaluation results for Grey-12b are yet to be determined. Users are encouraged to contribute their findings and benchmarks.
+
+## Limitations and Biases
+The model may exhibit biases present in its training data and constituent models. It's crucial to critically evaluate the model's outputs and use them in conjunction with human judgment.
+
+## Additional Information
+For more details on the base model and constituent models, please refer to their respective model cards and documentation.
+
+## Acknowledgments 🙏
+We acknowledge the contributions of:
+- VAGOsolutions for the SauerkrautLM-Nemo-12b-Instruct model
+- Cognitive Computations for the dolphin-2.9.3-mistral-nemo-12b model
+
+## How to Use
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("aixonlab/Grey-12b")
+tokenizer = AutoTokenizer.from_pretrained("aixonlab/Grey-12b")
+
+prompt = "Once upon a time"
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+
+generated_ids = model.generate(input_ids, max_length=100)
+generated_text = tokenizer.decode(generated_ids, skip_special_tokens=True)
+print(generated_text)