GGUF
conversational
How to use from the
Use from the
llama-cpp-python library
# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Odontome/LFM2.5-1.2B-Elm",
	filename="LFM2.5-1.2B-Elm-Q4_K_M.gguf",
)
llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

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LFM2.5-1.2B-Elm

Model Description

LFM2.5-1.2B-Elm is a fine-tuned version of LiquidAI/LFM2.5-1.2B-Thinking.

The agentic capabilities have been specifically enhanced for the Hermes-Agent harness. This model was trained using the following datasets:

  • kai-os/carnice-glm5-hermes-traces
  • lambda/hermes-agent-reasoning-traces

Performance & Benchmarks

This model is optimized for speed and efficiency. Inference speeds were measured using llama.cpp (CUDA v2.11.0) within LM Studio:

Hardware Tokens/s Notes
RTX 3090 ~506 Tok/s Blazing fast inference
RTX 4060 Laptop ~250 Tok/s Excellent portability performance
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GGUF
Model size
1B params
Architecture
lfm2
Hardware compatibility
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