Post
I finished my model merging experiment day.🤗I would love your thoughts on this.
What did I do? I merged Mistral Instruct 0.1 and 0.2 models using different merging techniques:
- SLERP: linear interpolation (most popular method)
- MoE: replace some forward layers with MoE layers; using a random gate for now
- Frankenmerge: also known as passthrough, but that isn't very cool. It concatenates some specified layers ending in different numbers of params. In my case, I went from 7B to 9B.
Note: merging is not building an ensemble of models. You can read more about merging techniques at https://huggingface.co/blog/mlabonne/merge-models
Results
I built the 3 models using mergekit (running in an HF Space) - took less than an hour to do the three) osanseviero/mistral-instruct-merges-659ebf35ca0781acdb86bb0a
I'm doing a quick check with the OpenLLM Leaderboard.
🚨The OpenLLM Leaderboard is more suitable for pre-trained models than instruct models, but I still thought it would be interesting to look at the insights🚨
You can look at the attached image. Some interesting things
- All three models performed somewhere between 0.1 and 0.2 - congrats to the 140 people who got it right in https://twitter.com/osanseviero/status/1745071548866736171
- Frankenmerge terribly sucked with GSM8K. It seems that adding some Mistral 0.1 layers actually degraded the performance a lot - this is worse than even 0.1!
- Otherwise, frankenmerge was decent across HellaSwag, MMLU, and specially TruthfulQA
- MoE is using random gating, so I expected something right in between 0.1 and 0.2, which was the case
What do I do with this?
Not sure tbh! I think doing proper MT bench evals would be nice. I also think all of us should give a nice GH star to mergekit because it's awesome. I would love to have the time to do end-to-end ablation studies, but cool new things are coming up. Let me know if you have any thoughts in the results
What did I do? I merged Mistral Instruct 0.1 and 0.2 models using different merging techniques:
- SLERP: linear interpolation (most popular method)
- MoE: replace some forward layers with MoE layers; using a random gate for now
- Frankenmerge: also known as passthrough, but that isn't very cool. It concatenates some specified layers ending in different numbers of params. In my case, I went from 7B to 9B.
Note: merging is not building an ensemble of models. You can read more about merging techniques at https://huggingface.co/blog/mlabonne/merge-models
Results
I built the 3 models using mergekit (running in an HF Space) - took less than an hour to do the three) osanseviero/mistral-instruct-merges-659ebf35ca0781acdb86bb0a
I'm doing a quick check with the OpenLLM Leaderboard.
🚨The OpenLLM Leaderboard is more suitable for pre-trained models than instruct models, but I still thought it would be interesting to look at the insights🚨
You can look at the attached image. Some interesting things
- All three models performed somewhere between 0.1 and 0.2 - congrats to the 140 people who got it right in https://twitter.com/osanseviero/status/1745071548866736171
- Frankenmerge terribly sucked with GSM8K. It seems that adding some Mistral 0.1 layers actually degraded the performance a lot - this is worse than even 0.1!
- Otherwise, frankenmerge was decent across HellaSwag, MMLU, and specially TruthfulQA
- MoE is using random gating, so I expected something right in between 0.1 and 0.2, which was the case
What do I do with this?
Not sure tbh! I think doing proper MT bench evals would be nice. I also think all of us should give a nice GH star to mergekit because it's awesome. I would love to have the time to do end-to-end ablation studies, but cool new things are coming up. Let me know if you have any thoughts in the results
