lfm2.5:8b-a1b-q4_K

lfm2.5:8b-a1b-q4_K_M

2,449 Downloads Updated 3 days ago

LFM2.5-8B-A1B, an edge model built for fast, reliable tool calling on consumer hardware.

tools thinking 8b

ollama run lfm2.5:8b-a1b-q4_K_M

curl http://localhost:11434/api/chat \
  -d '{
    "model": "lfm2.5:8b-a1b-q4_K_M",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='lfm2.5:8b-a1b-q4_K_M',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'lfm2.5:8b-a1b-q4_K_M',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Details

Updated 3 days ago

3 days ago

9cf756159fc2 · 5.2GB ·

model

archlfm2moe

parameters8.47B

quantizationQ4_K_M

5.2GB

license

LFM Open License v1.0 TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "

11kB

params

{ "repeat_penalty": 1.05, "temperature": 0.2, "top_k": 80 }

53B

Readme

LFM2.5 is a new family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.

On-device personal assistant: Designed to power real-life applications, chaining tool calls, and following complex instructions on all devices.
Compressed performance: Competitive with much larger dense and MoE models on instruction following and agentic tasks.
Unmatched throughput: Fastest in its size class on both CPU and GPU inference