LFM2-1.2B-Tool — Ollama Model (8-16 GB GPU) Tool-Calling Specialized

Model: oamazonasgabriel/lfm2-1.2b-tool:f16-8gbGPU

DESCRIPTION

LFM2-1.2B-Tool is a specialized variant of Liquid AI’s LFM2 architecture, fine-tuned exclusively for concise and precise tool calling. It combines 10 double-gated Linear-Invariant-Valued (LIV) convolution blocks with 6 full-attention Grouped-Query Attention (GQA) blocks — a novel hybrid design that achieves 2-3x better perplexity per parameter than dense transformers. The model is designed to outperform similarly sized thinking models for function-calling tasks without any internal chain-of-thought overhead, delivering instant tool call generation for latency-sensitive applications. This Ollama packaging uses the official F16 GGUF quantization (~2.5 GB).

“Built to Run Anywhere.” — Liquid AI

The ultimate tool-calling companion for: 8 GB laptops · 16 GB desktops · Edge servers · Real-time assistants · IoT devices · API orchestration

Key Features

• Purpose-built for tool calling: outperforms thinking models of similar size on function-calling benchmarks
• No chain-of-thought overhead: instant tool call generation — ideal for latency-critical applications
• Greedy decoding (temp=0): deterministic output guarantees syntactically valid function calls every time
• Full precision: F16 weights preserve 100% model quality (zero quantization loss)
• 128K native context (configured to 32K for 8 GB, expandable to 128K on 16 GB+)
• Pythonic function calls: native format uses Python syntax func(arg="value") — natural and intuitive
• Ollama API compatible: OpenAI-compatible tool schemas are automatically translated to native format
• Multi-turn support: handles complex tool chains across multiple conversation turns
• 9 languages: EN, AR, ZH, FR, DE, JA, KO, PT, ES
• LFM Open License v1.0: royalty-free for non-commercial/research and small business (<$10M annual revenue)

Architecture

REQUIREMENTS

Platform support: - CPU-only — runs on any modern CPU (efficient hybrid architecture) - Any GPU (NVIDIA, AMD, Intel Arc, Apple Silicon) with 2 GB+ VRAM - Edge servers & IoT — purpose-built for edge deployment

💡 Why 8-16 GB RAM? The model is ~2.5 GB. An 8 GB machine has ~4.2 GB of headroom — enough to run alongside your other applications. On 16 GB, you have ~11 GB headroom for larger contexts.

QUICK START

1. Install Ollama

# macOS (Homebrew)
brew install ollama

# Linux
curl -fsSL https://ollama.com/install.sh | sh

# Windows
# Download from https://ollama.com/download

2. Pull & Run

# Pull the model (downloads ~2.5 GB)
ollama pull oamazonasgabriel/lfm2-1.2b-tool:f16-8gbGPU

# Run interactively
ollama run oamazonasgabriel/lfm2-1.2b-tool:f16-8gbGPU

USAGE

CLI

# Interactive chat (tool calling via API recommended)
ollama run oamazonasgabriel/lfm2-1.2b-tool:f16-8gbGPU

# With system prompt providing tools
ollama run oamazonasgabriel/lfm2-1.2b-tool:f16-8gbGPU \
  --system "You are a tool-calling assistant. Use the available tools to answer questions."

# Override context for 16 GB systems
ollama run oamazonasgabriel/lfm2-1.2b-tool:f16-8gbGPU \
  --num-ctx 65536

REST API — Tool Calling (Primary Use Case)

# Single tool call
curl -s http://127.0.0.1:11434/api/chat \
  -d '{
    "model": "oamazonasgabriel/lfm2-1.2b-tool:f16-8gbGPU",
    "messages": [
      {"role": "user", "content": "What is the weather in Boston?"}
    ],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "get_weather",
          "description": "Get the current weather for a city",
          "parameters": {
            "type": "object",
            "properties": {
              "location": {"type": "string", "description": "City name"}
            },
            "required": ["location"]
          }
        }
      }
    ],
    "options": {
      "temperature": 0.0
    }
  }'

Multi-Turn Tool Calling

# Turn 1: User asks a question
curl -s http://127.0.0.1:11434/api/chat \
  -d '{
    "model": "oamazonasgabriel/lfm2-1.2b-tool:f16-8gbGPU",
    "messages": [
      {"role": "user", "content": "Find me a hotel in San Francisco for July 4-6 with a pool"}
    ],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "search_hotels",
          "description": "Search hotels by location, dates, and amenities",
          "parameters": {
            "type": "object",
            "properties": {
              "location": {"type": "string"},
              "check_in": {"type": "string"},
              "check_out": {"type": "string"},
              "amenities": {"type": "array", "items": {"type": "string"}}
            },
            "required": ["location", "check_in", "check_out"]
          }
        }
      }
    ],
    "options": {"temperature": 0.0}
  }'

# Then feed the tool response back to get the final answer
curl -s http://127.0.0.1:11434/api/chat \
  -d '{
    "model": "oamazonasgabriel/lfm2-1.2b-tool:f16-8gbGPU",
    "messages": [
      {"role": "user", "content": "Find me a hotel in San Francisco for July 4-6 with a pool"},
      {"role": "assistant", "content": "",
       "tool_calls": [{"function": {"name": "search_hotels", "arguments": {"location": "San Francisco", "check_in": "2024-07-04", "check_out": "2024-07-06", "amenities": ["pool"]}}}]
      },
      {"role": "tool", "content": "{\"hotels\": [{\"name\": \"Marriott Marquis\", \"pool\": true, \"price\": 299}, {\"name\": \"Hilton Union Square\", \"pool\": true, \"price\": 249}]}"}
    ],
    "options": {"temperature": 0.0}
  }'

Basic Chat (No Tools)

curl -s http://127.0.0.1:11434/api/chat \
  -d '{
    "model": "oamazonasgabriel/lfm2-1.2b-tool:f16-8gbGPU",
    "messages": [{"role": "user", "content": "Hello! What can you do?"}]
  }'

OpenAI-Compatible Endpoint

curl -s http://127.0.0.1:11434/v1/chat/completions \
  -d '{
    "model": "oamazonasgabriel/lfm2-1.2b-tool:f16-8gbGPU",
    "messages": [{"role": "user", "content": "Get me the stock price for AAPL."}],
    "tools": [
      {
        "type": "function",
        "function": {
          "name": "get_stock_price",
          "description": "Get current stock price",
          "parameters": {
            "type": "object",
            "properties": {
              "symbol": {"type": "string"}
            },
            "required": ["symbol"]
          }
        }
      }
    ],
    "stream": false
  }'

Python (ollama library)

pip install ollama

import ollama

# Tool calling
response = ollama.chat(
    model='oamazonasgabriel/lfm2-1.2b-tool:f16-8gbGPU',
    messages=[{'role': 'user', 'content': 'What is the weather in Boston?'}],
    tools=[{
        'type': 'function',
        'function': {
            'name': 'get_weather',
            'description': 'Get current weather',
            'parameters': {
                'type': 'object',
                'properties': {
                    'location': {'type': 'string', 'description': 'City name'}
                },
                'required': ['location']
            }
        }
    }],
)
print(response.message.tool_calls)

# Multi-turn with tool response
response = ollama.chat(
    model='oamazonasgabriel/lfm2-1.2b-tool:f16-8gbGPU',
    messages=[
        {'role': 'user', 'content': 'What is the weather in Boston?'},
        {'role': 'assistant', 'content': '', 'tool_calls': [{'function': {'name': 'get_weather', 'arguments': {'location': 'Boston'}}}]},
        {'role': 'tool', 'content': '{"temperature": 72, "conditions": "sunny"}'},
    ],
)
print(response.message.content)

JavaScript (ollama.js)

npm install ollama

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'oamazonasgabriel/lfm2-1.2b-tool:f16-8gbGPU',
  messages: [{ role: 'user', content: 'Get the stock price for AAPL.' }],
  tools: [{
    type: 'function',
    function: {
      name: 'get_stock_price',
      description: 'Get current stock price',
      parameters: {
        type: 'object',
        properties: {
          symbol: { type: 'string' }
        },
        required: ['symbol']
      }
    }
  }],
})
console.log(response.message.tool_calls)

SAMPLING PARAMETERS

These are baked into the model via its Modelfile:

⚠️ Important: Always use temperature=0.0 for production tool-calling pipelines. Higher temperatures may introduce syntax errors in Pythonic function calls.

Equivalent llama.cpp Command

./llama-cli \
  -m LFM2-1.2B-Tool-F16.gguf \
  -ngl 99 \
  -c 32768 \
  --temp 0.0 \
  --top-k 50 \
  --repeat-penalty 1.05 \
  -cnv

MEMORY & PERFORMANCE

RAM Budget

Context Window Scaling (8 GB, with q8_0 KV cache)

Performance

TROUBLESHOOTING

Optimizations for 8 GB Systems

# Set KV cache to q8_0 for ~50% memory reduction
export OLLAMA_KV_CACHE_TYPE=q8_0
ollama serve

# In another terminal:
ollama run oamazonasgabriel/lfm2-1.2b-tool:f16-8gbGPU

# Or reduce context at runtime:
ollama run oamazonasgabriel/lfm2-1.2b-tool:f16-8gbGPU --num-ctx 16384

ALTERNATIVE MODELS

# Ultra-lightweight version of this family (4-8 GB)
ollama pull oamazonasgabriel/lfm2.5-230m:bf16-8gbGPU

# Larger general-purpose model (needs 16 GB)
ollama pull oamazonasgabriel/qwen3.5-9b:q4-16gbGPU

# Larger MoE model (needs 24 GB)
ollama pull oamazonasgabriel/qwen3.6-35b-a3b:q4-24gbGPU

# Same architecture, general instruct version
ollama pull hf.co/LiquidAI/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M

# Quantized version of this model (smaller, faster)
ollama pull hf.co/LiquidAI/LFM2-1.2B-Tool-GGUF:Q4_K_M

CREDITS

This Ollama packaging brings together work from multiple teams and individuals:

Citation

If you use this model in your work, please cite the original LFM2 technical report:

@article{liquidai2025lfm2,
  title={LFM2 Technical Report},
  author={Liquid AI},
  journal={arXiv preprint arXiv:2511.23404},
  year={2025}
}

@misc{liquidAI2026tool,
  author = {Liquid AI},
  title = {LFM2-1.2B-Tool: Concise and Precise Tool Calling for Edge Devices},
  year = {2026},
  note = {https://huggingface.co/LiquidAI/LFM2-1.2B-Tool}
}

Acknowledgments

• The llama.cpp project (github.com/ggerganov/llama.cpp) for the GGUF format and efficient inference engine that makes models like this accessible on commodity hardware.
• The Ollama team (ollama.com) for the seamless model distribution platform.
• Hugging Face (huggingface.co) for model hosting and the Hub ecosystem.

LINKS