---
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).