damfle/ornith-9b-custom

Ornith-9B Custom

A fine-tuned version of Ornith-1.0-9B by Damien FLETY.

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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)

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Intended Use

This model is designed for:

• Efficient inference in quantized (4-bit) form. (soon)
• Integration into RAG (Retrieval-Augmented Generation) pipelines.

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Training Configuration

• Dataset: Custom dataset (details not specified).
• Training Approach:
  • Quantization-Aware Training (QAT) for 4-bit inference.
• Optimizer: Muon optimizer (preferred for efficiency).

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Performance

• Inference: Optimized for low-latency, high-throughput inference in quantized form. (dspark soon)

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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.

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Acknowledgments

• Base model: Ornith-1.0-9B by Ornith AI.
• Fine-tuning and optimizations by Damien FLETY.