Introducing our first standalone model – FluentlyLM Prinum
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Introducing our first standalone model – FluentlyLM Prinum
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Introducing the first standalone model from Project Fluently LM! We worked on it for several months, used different approaches and eventually found the optimal one.
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Introducing the first standalone model from Project Fluently LM! We worked on it for several months, used different approaches and eventually found the optimal one.
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General characteristics:
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General characteristics:
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- Model type: Causal language models (QwenForCausalLM, LM Transformer)
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- Model type: Causal language models (QwenForCausalLM, LM Transformer)
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- Number of parameters: 32.5B
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- Number of parameters: 32.5B
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- Number of parameters (not embedded): 31.0B
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- Number of parameters (not embedded): 31.0B
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- Number of layers: 64
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- Number of layers: 64
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- Context: 131,072 tokens
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- Context: 131,072 tokens
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- Language(s) (NLP): English, French, Spanish, Russian, Chinese, Japanese, Persian (officially supported)
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- Language(s) (NLP): English, French, Spanish, Russian, Chinese, Japanese, Persian (officially supported)
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- License: MIT
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- License: MIT
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Creation strategy:
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Creation strategy:
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The basis of the strategy is shown in Pic. 2.
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The basis of the strategy is shown in Pic. 2.
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We used Axolotl & Unsloth for SFT-finetuning with PEFT LoRA (rank=64, alpha=64) and Mergekit for SLERP and TIES mergers.
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We used Axolotl & Unsloth for SFT-finetuning with PEFT LoRA (rank=64, alpha=64) and Mergekit for SLERP and TIES mergers.
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Evolution:
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Evolution:
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🏆 12th place in the Open LLM Leaderboard (
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🏆 12th place in the Open LLM Leaderboard (
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#
open-llm-leaderboard/open_llm_leaderboard
space
resource
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#
) (21.02.2025)
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) (21.02.2025)
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Detailed results and comparisons are presented in Pic. 3.
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Detailed results and comparisons are presented in Pic. 3.
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Links:
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Links:
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- Model: 
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- Model: 
https://huggingface.co/fluently-lm/FluentlyLM-Prinum
https://huggingface.co/fluently-lm/FluentlyLM-Prinum
link
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- GGUF version: 
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- GGUF version: 
https://huggingface.co/mradermacher/FluentlyLM-Prinum-GGUF
mradermacher/FluentlyLM-Prinum-GGUF
model
resource
https://huggingface.co/mradermacher/FluentlyLM-Prinum-GGUF
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- Demo on ZeroGPU: 
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- Demo on ZeroGPU: 
https://huggingface.co/spaces/ehristoforu/FluentlyLM-Prinum-demo
ehristoforu/FluentlyLM-Prinum-demo
space
resource
https://huggingface.co/spaces/ehristoforu/FluentlyLM-Prinum-demo
Introducing our first standalone model – FluentlyLM Prinum

Introducing the first standalone model from Project Fluently LM! We worked on it for several months, used different approaches and eventually found the optimal one.

General characteristics:
- Model type: Causal language models (QwenForCausalLM, LM Transformer)
- Number of parameters: 32.5B
- Number of parameters (not embedded): 31.0B
- Number of layers: 64
- Context: 131,072 tokens
- Language(s) (NLP): English, French, Spanish, Russian, Chinese, Japanese, Persian (officially supported)
- License: MIT

Creation strategy:
The basis of the strategy is shown in Pic. 2.
We used Axolotl & Unsloth for SFT-finetuning with PEFT LoRA (rank=64, alpha=64) and Mergekit for SLERP and TIES mergers.

Evolution:
🏆 12th place in the Open LLM Leaderboard (https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#) (21.02.2025)

Detailed results and comparisons are presented in Pic. 3.

Links:
- Model: https://huggingface.co/fluently-lm/FluentlyLM-Prinum
- GGUF version: https://huggingface.co/mradermacher/FluentlyLM-Prinum-GGUF
- Demo on ZeroGPU: https://huggingface.co/spaces/ehristoforu/FluentlyLM-Prinum-demo
🔥
ehristoforu John6666 jeffcookio KingNish dantezxcd
6758a9850e3fff481964ca6d
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/EolfJfjW25hC4Bt_hCPq8.png
Jean Louis
JLouisBiz
user
65a3d8d58448f47df24c041a
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/o-5N9QyjHgmSMk69e3O55.png
Evgeniy Hristoforu
ehristoforu
user