Zinakha-12b 🧙♂️
Zinakha 12b tries to become the perfect companion for any chat which involves multiple roles. The ability to understand context is pretty awesome and excels in creativity and storytelling. It is built on Nemo 12b and trained on different datasets as well as some layer merges to ehance its capabilities.
Model Details 📊
- Developed by: Aixon Lab
- Model type: Causal Language Model
- Language(s): English (primarily), may support other languages
- License: Apache 2.0
- Repository: https://huggingface.co/aixonlab/Zinakha-12b
Quantization
- GGUF: https://huggingface.co/mradermacher/Zinakha-12b-GGUF
- iMatrix GGUF: https://huggingface.co/mradermacher/Zinakha-12b-i1-GGUF
Model Architecture 🏗️
- Base model: mistralai/Mistral-Nemo-Base-2407
- Parameter count: ~12 billion
- Architecture specifics: Transformer-based language model
Intended Use 🎯
As an advanced language model for various natural language processing tasks, including but not limited to text generation (excels in chat), question-answering, and analysis.
Ethical Considerations 🤔
As a model based on multiple sources, Zinakha-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 Zinakha-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.
How to Use
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("aixonlab/Zinakha-12b")
tokenizer = AutoTokenizer.from_pretrained("aixonlab/Zinakha-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)