Can you merge models of different sizes? ⚗️
text
Can you merge models of different sizes? ⚗️
new_line
new_line
Well, yes, if the models are somewhat compatible. Here is an experiment I did. I wanted to merge two of the best performing models: 
text
Well, yes, if the models are somewhat compatible. Here is an experiment I did. I wanted to merge two of the best performing models: 
https://huggingface.co/mlabonne/NeuralBeagle14-7B
mlabonne/NeuralBeagle14-7B
model
resource
https://huggingface.co/mlabonne/NeuralBeagle14-7B
 and 
text
 and 
https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v4
jeonsworld/CarbonVillain-en-10.7B-v4
model
resource
https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v4
new_line
new_line
Here is my recipe:
text
Here is my recipe:
new_line
1. Expand the layers of NeuralBeagle to 10.7B ala frankenmerge.
text
1. Expand the layers of NeuralBeagle to 10.7B ala frankenmerge.
new_line
2. DPO-tune the previous model with a high-quality preference dataset, 
text
2. DPO-tune the previous model with a high-quality preference dataset, 
https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
argilla/distilabel-intel-orca-dpo-pairs
dataset
resource
https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
new_line
3. Merge the previous model with CarbonVillain (needs —allow-crimes in mergekit! 🔪)
text
3. Merge the previous model with CarbonVillain (needs —allow-crimes in mergekit! 🔪)
new_line
new_line
And here is the resulting model, CarbonBeagle-11B, which ranked top in the leaderboard for its size class:
text
And here is the resulting model, CarbonBeagle-11B, which ranked top in the leaderboard for its size class:
new_line
https://huggingface.co/vicgalle/CarbonBeagle-11B
vicgalle/CarbonBeagle-11B
model
resource
https://huggingface.co/vicgalle/CarbonBeagle-11B
new_line
Can you merge models of different sizes? ⚗️

Well, yes, if the models are somewhat compatible. Here is an experiment I did. I wanted to merge two of the best performing models: https://huggingface.co/mlabonne/NeuralBeagle14-7B and https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v4

Here is my recipe:
1. Expand the layers of NeuralBeagle to 10.7B ala frankenmerge.
2. DPO-tune the previous model with a high-quality preference dataset, https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
3. Merge the previous model with CarbonVillain (needs —allow-crimes in mergekit! 🔪)

And here is the resulting model, CarbonBeagle-11B, which ranked top in the leaderboard for its size class:
https://huggingface.co/vicgalle/CarbonBeagle-11B

🤝
vicgalle victor samusenps NeuralNovel itsn78 merve Dlbk clem antiven0m lunarflu Svngoku Makya mlabonne tryumanshow
🤯
osanseviero itsn78 merve clem cbensimon lunarflu luqmanxyz vicgalle Makya roinaveiro
6536dba326ec79fe53aee18e
https://cdn-avatars.huggingface.co/v1/production/uploads/6536dba326ec79fe53aee18e/eINj0F5JEYYdCZTrjBvu4.png
limin(gate)
gate369
user
5fad8602b8423e1d80b8a965
https://cdn-avatars.huggingface.co/v1/production/uploads/5fad8602b8423e1d80b8a965/tRqTwcZmrGka8c1vFq2wX.jpeg
Victor Gallego
vicgalle
user