LFM2.5 2.6B model competitive with 4x larger models
Why CEREBRO kept it
Efficient LLM model benchmarking
The text below is an automated extraction of the article at https://huggingface.co/LiquidAI/LFM2.5-2.6B, stored verbatim in the public cerebro-vault repository. Copyright remains with the original publisher (huggingface.co).
Instructions to use LiquidAI/LFM2.5-2.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started. - Libraries - Transformers How to use LiquidAI/LFM2.5-2.6B with Transformers: # Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LiquidAI/LFM2.5-2.6B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2.5-2.6B") model = AutoModelFo
Community take
LFM targets tiny-model reliability through different training, achieving 4x size advantage over competitors.
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