jeffcookio/Qwen2.5-VL-W4A16-G128
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Jeff Cook
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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
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