Ornith-9B Custom
A fine-tuned version of Ornith-1.0-9B by Damien FLETY.
Model Details
- Base Model: Ornith-1.0-9B
- Fine-tuned by: damfle
- License: Inherits the license of the base model (check 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)
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 by Ornith AI.
- Fine-tuning and optimizations by Damien FLETY.