Source: https://huggingface.co/LiquidAI/LFM2.5-350M-GGUF

ollama run jewelzufo/LFM2.5-350M-GGUF

curl http://localhost:11434/api/chat \
  -d '{
    "model": "jewelzufo/LFM2.5-350M-GGUF",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='jewelzufo/LFM2.5-350M-GGUF',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'jewelzufo/LFM2.5-350M-GGUF',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Models

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Name

1 model

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LFM2.5-350M-GGUF:latest

267MB · 125K context window · Text · 1 month ago

LFM2.5-350M-GGUF:latest

267MB

125K

Text

Readme

Try LFM • Docs • LEAP • Discord

LFM2.5-350M-GGUF

LFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency.

Find more details in the original model card: https://huggingface.co/LiquidAI/LFM2.5-350M

🏃 How to run LFM2

Example usage with llama.cpp:

llama-cli -hf LiquidAI/LFM2.5-350M-GGUF

<div align="center">
  <img 
    src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png" 
    alt="Liquid AI" 
    style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
  />
  <div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
    <a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • 
    <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • 
    <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • 
    <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
  </div>
</div>
<br>

# LFM2.5-350M-GGUF

LFM2 is a new generation of hybrid models developed by [Liquid AI](https://www.liquid.ai/), specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency.

Find more details in the original model card: https://huggingface.co/LiquidAI/LFM2.5-350M

## 🏃 How to run LFM2

Example usage with [llama.cpp](https://github.com/ggml-org/llama.cpp):

```
llama-cli -hf LiquidAI/LFM2.5-350M-GGUF
```

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