初始化项目，由ModelHub XC社区提供模型

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

README.md

@@ -0,0 +1,61 @@
+---
+library_name: transformers
+license: apache-2.0
+base_model:
+- mistralai/Mistral-Nemo-Base-2407
+---
+
+![Zinakha-12b Banner](https://cdn-uploads.huggingface.co/production/uploads/66dcee3321f901b049f48002/tIP5l3rHfawQwNg0PvgUN.png)
+
+# 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 
+```python
+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)