Ornith-1.0-9B-Engineering
Standalone merged Hugging Face Transformers model created by merging the selected Epoch 2 engineering QLoRA adapter into ornith-ai/Ornith-1.0-9B.
Lineage
ornith-ai/Ornith-1.0-9B + Epoch 2 QLoRA adapter (checkpoint-1072) -> validated standalone merged model.
The original public GGUF model was not used for training or merging.
Training And Selection
- Training examples: 17,133
- Frozen evaluation examples: 902
- Method: QLoRA adapter training, then merge into the full-precision Transformers checkpoint
- Selected checkpoint: Epoch 2 (
checkpoint-1072)
Validation loss:
| Epoch | Validation loss |
|---|---|
| 1 | 1.3532 |
| 2 | 1.2940 |
| 3 | 1.4327 |
Epoch 2 was selected because it had the best held-out token F1 and the lowest validation loss. Epoch 3 had one additional exact match, but lower token F1 and higher validation loss.
Frozen Evaluation
All results below use exactly the same frozen 902-example evaluation set.
| Model | Exact match | Normalized exact match | Token F1 |
|---|---|---|---|
| Base Ornith-1.0-9B | 0.0000 | 0.0000 | 0.1312 |
| Epoch 2 adapter | 0.0022 | 0.0022 | 0.3272 |
| Epoch 3 adapter | 0.0033 | 0.0033 | 0.2942 |
| Merged Epoch 2 model | 0.0055 | 0.0055 | 0.3429 |
The merged model was evaluated as a standalone model on all 902 frozen examples.
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "Sara121/Ornith-1.0-9B-Engineering"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
trust_remote_code=True,
device_map="auto",
torch_dtype=torch.bfloat16,
)
model.eval()
Use the bundled tokenizer and chat template. Ornith generation conventions may include <think>...</think> reasoning content before the answer.
Limitations
This is a domain-adapted model for engineering QA and should be validated before use in production or compliance-sensitive workflows. Evaluation metrics are lexical and do not guarantee factual or regulatory correctness.