Merging
Your model seems to have a similar score and reasoning approach to VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct. Did you merge your model with that one?
Hi @DeepMount00
No, it's not a merge with any model outside my own fine-tuned models. Back then we didn't have that many 8B models unlike Mistral 7B, so I fine-tuned my own. If I remember correctly, this specific model is a merge between v0.4 and another version of my own model and then it went through a DPO fine-tuning.
I have moved on to making qwen2 models, but I can try to do some merging with VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
and other models. (now that we have more 8B models out there)
Thank you for your response, @MaziyarPanahi . I am curious about the dataset you used for training this model. It performs exceptionally well in the Italian language, and since I am conducting research in this area, I would appreciate it if you could share the dataset you used.
This is very interesting! I've never noticed nor intended to improve multi-lingual capability of any of my fine-tuned models. The answers in Italian language, is it just better in terms of writing style and vibe or is more correct compare to the 8B Instruct?
I used some public DPO datasets, and if I am not mistaken 1 private one that I know it's multi-lingual, but never noticed that it might improve other languages.
I compared your model with Llama 8B Instruct, and found that your model performs better in the Italian language in several key areas:
- The Italian text generation is more grammatically and semantically accurate.
- It has a superior understanding of sentence nuances, resulting in more appropriate and relevant responses.
Could you please provide the name of the public dataset used for fine-tuning?
interesting! never noticed that, thanks for sharing.
Since it's a merge of multiple fine-tuned models of mine, the public ones are the usual ones:
- Orca
- OpenHermes
- Ultrafeedback
- Math