Update README.md

README.md

@@ -1,3 +1,85 @@
 ---
 license: isc
+datasets:
+- damfle/private-multistral-compiled-datasets
+- mlabonne/open-perfectblend
+language:
+- fr
+- en
+base_model:
+- ornith-ai/Ornith-1.0-9B
+library_name: transformers
 ---
+# Ornith-9B Custom
+
+A fine-tuned version of [Ornith-1.0-9B](https://huggingface.co/ornith-ai/Ornith-1.0-9B) by [Damien FLETY](https://huggingface.co/damfle).
+
+---
+
+## Model Details
+
+- **Base Model**: [Ornith-1.0-9B](https://huggingface.co/ornith-ai/Ornith-1.0-9B)
+- **Fine-tuned by**: [damfle](https://huggingface.co/damfle)
+- **License**: Inherits the license of the base model (check [Ornith-1.0-9B](https://huggingface.co/ornith-ai/Ornith-1.0-9B) for details).
+- **Quantization**: Optimized for 4-bit quantization (QAT) and FP8 training. (soon)
+
+---
+
+## Intended Use
+
+This model is designed for:
+
+- Efficient inference in quantized (4-bit) form. (soon)
+- Integration into RAG (Retrieval-Augmented Generation) pipelines.
+
+---
+
+## Training Configuration
+
+- **Dataset**: Custom dataset (details not specified).
+- **Training Approach**:
+  - Quantization-Aware Training (QAT) for 4-bit inference.
+- **Optimizer**: Muon optimizer (preferred for efficiency).
+
+---
+
+## Performance
+
+- **Inference**: Optimized for low-latency, high-throughput inference in quantized form. (dspark soon)
+
+---
+
+## How to Use
+
+### Inference (4-bit Quantized)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "damfle/ornith-9b-custom"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.float16
+)
+
+input_text = "Your prompt here"
+inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+
+## Notes
+
+- This model is part of an iterative process to merge embeddings for a transformers architecture while keeping embedding models separate for RAG.
+- Future plans include scaling to a 16B QAT 4-bit model.
+
+---
+
+## Acknowledgments
+
+- Base model: [Ornith-1.0-9B](https://huggingface.co/ornith-ai/Ornith-1.0-9B) by [Ornith AI](https://huggingface.co/ornith-ai).
+- Fine-tuning and optimizations by [Damien FLETY](https://huggingface.co/damfle).