File size: 2,308 Bytes
09260cc fafd7c4 09260cc fafd7c4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 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). |