---

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, 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:

ollama run hf.co/jamesatron1512/LFM2.5-350M-GGUF

Or specify the quantization tag explicitly:

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

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"])