2 7 284

Jeff Cook

jeffcookio

sjuxax

AI & ML interests

None yet

Recent Activity

reacted to ehristoforu's post with 🔥 4 days ago

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

liked a model 4 days ago

GAIR/LIMO

liked a dataset 4 days ago

GeneralReasoning/GeneralThought-Feb25

View all activity

Organizations

None yet

jeffcookio's activity

reacted to ehristoforu's post with 🔥 4 days ago

Post

2539

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 ( open-llm-leaderboard/open_llm_leaderboard) (21.02.2025)

Detailed results and comparisons are presented in Pic. 3.

Links:
- Model: fluently-lm/FluentlyLM-Prinum
- GGUF version: mradermacher/FluentlyLM-Prinum-GGUF
- Demo on ZeroGPU: ehristoforu/FluentlyLM-Prinum-demo