Instructions to use lewisdog/lfm2.5-350m-cogs-query-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use lewisdog/lfm2.5-350m-cogs-query-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0

Use Docker

docker model run hf.co/lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use lewisdog/lfm2.5-350m-cogs-query-GGUF with Ollama:
```
ollama run hf.co/lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0
```
Unsloth Desktop

How to use lewisdog/lfm2.5-350m-cogs-query-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use lewisdog/lfm2.5-350m-cogs-query-GGUF with Docker Model Runner:
```
docker model run hf.co/lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0
```

Lemonade

How to use lewisdog/lfm2.5-350m-cogs-query-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0

Run and chat with the model

lemonade run user.lfm2.5-350m-cogs-query-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use lewisdog/lfm2.5-350m-cogs-query-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use lewisdog/lfm2.5-350m-cogs-query-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "lewisdog/lfm2.5-350m-cogs-query-GGUF:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

LFM2.5-350M cogs query student (GGUF)

LoRA fine-tune of LiquidAI/LFM2.5-350M as the Cogitarium query student: translates a natural-language data request + schema into ONE LadybugDB Cypher query (openCypher-like, constrained single-MATCH grammar). Trained on 3,065 execution-verified pairs (teacher glm-5.2; every pair executed against a seeded store before admission).

Frozen 55-example execution eval (see the cogs-finetune-query runbook):

model	exec	match
base LFM2.5-350M few-shot	7.3%	0%
Qwen3-1.7B few-shot	94.5%	12.7%
this model (bf16)	98.2%	23.6%
this model (Q8_0)	96.4%	23.6%
this model (Q4_K_M)	63.6%	10.9%

Pins (measured): repetition penalty 1.0; Q8_0 quantization REQUIRED (Q4_K_M exhibits token corruption). Serve with the exact system prompts in Cogitarium src/data/prompts.ts (sha-pinned train/serve contract).

Downloads last month: 48

GGUF

Model size

0.4B params

Architecture

lfm2

Hardware compatibility

8-bit

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Model tree for lewisdog/lfm2.5-350m-cogs-query-GGUF

Base model

LiquidAI/LFM2.5-350M-Base

Finetuned

LiquidAI/LFM2.5-350M

Quantized

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