Model details
These are the q4 in GGUF of a quick experiment on llamafied phi-3 with only 1000 orpo steps from an azureml translated german orca binarized-dataset (johannhartmann/mistralorpo), with original phi-3 prompt template. The immediate result is not really good, but also not bad enough to disencourage further experiments.
Benchmark results
This was an experiment on a german dataset snippet which, as expected, worsened results on english benchmarks:
| Metric | Value |
|---|---|
| Avg. | 64.40 |
| AI2 Reasoning Challenge (25-Shot) | 60.41 |
| HellaSwag (10-Shot) | 78.37 |
| MMLU (5-Shot) | 65.26 |
| TruthfulQA (0-shot) | 49.76 |
| Winogrande (5-shot) | 70.24 |
| GSM8k (5-shot) | 62.32 |
On german EQ-Bench (v2_de) 51.82 (insignificant over 51.41 for original llamafied but significantly better than intermediate cstr/phi-3-orpo-v8_16 which after initial 150 test steps achieved 46.38) but with still only 164/171 correctly parsed.
Note: We can improve the correctness of parsing, i.a., by only a few SFT steps, as shown with cas/phi3-mini-4k-llamafied-sft-v3 (170/171 correct but with then only 39.46 score in v2_de, which was also an experiment in changing the prompt template). All that was quickly done with bnb and q4 quants only, which might, in theory, affect especially such small dense models significantly. But it served the intention for both proof-of-concept-experiments at least. Probably it would easily be possible to further improve results, but that would take some time and compute.
Training setup
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
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