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license: other
license_name: lfm1.0
license_link: https://huggingface.co/LiquidAI/LFM2.5-350M/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- liquid
- lfm2.5
- gguf
- ollama
- edge
- conversational
base_model: LiquidAI/LFM2.5-350M
---
# LiquidAI LFM2.5-350M (Instruct) - GGUF (Q4_K_M)
This repository provides the quantized **Q4_K_M GGUF** weights for **[LiquidAI/LFM2.5-350M](https://huggingface.co/LiquidAI/LFM2.5-350M)**, configured for direct 1-click execution in **Ollama**, **llama.cpp**, and local edge devices.
LFM2.5-350M is a hybrid architecture developed by Liquid AI combining double-gated short convolutions with structured attention for near-linear computational scaling and low memory footprint.
---
## ⚡ Direct Ollama Run (1-Line Command)
You can run this model directly via Ollama without manually downloading any files:
```bash
ollama run hf.co/jamesatron1512/LFM2.5-350M-GGUF
```
Or specify the quantization tag explicitly:
```bash
ollama run hf.co/jamesatron1512/LFM2.5-350M-GGUF:Q4_K_M
```
---
## 🚀 Model Details
- **Parameters**: 350 Million
- **Precision**: Q4_K_M (Quantized 4-bit)
- **File Size**: ~219 MB
- **Context Length**: Up to 128k tokens (default 4096 in Modelfile)
- **Chat Template**: ChatML format (`<|im_start|>user ... <|im_end|>`)
- **System Prompt**: Supported via template, default is left clean to prevent fixation on small parameter counts.
---
## 💻 Python API Usage via Ollama
```python
import requests
response = requests.post(
"http://localhost:11434/api/generate",
json={
"model": "hf.co/jamesatron1512/LFM2.5-350M-GGUF",
"prompt": "Explain quantum computing in two sentences.",
"stream": False,
"options": {
"temperature": 0.7,
"top_p": 0.9,
"num_predict": 128
}
}
)
print(response.json()["response"])
```
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