Instructions to use DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-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 DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-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 DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS

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 DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS

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 DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS

Use Docker

docker model run hf.co/DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS

LM Studio
Jan

vLLM

How to use DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS

Ollama
How to use DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF with Ollama:
```
ollama run hf.co/DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS
```
Unsloth Desktop

How to use DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS

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": "DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF with Docker Model Runner:
```
docker model run hf.co/DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS
```

Lemonade

How to use DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF-IQ4_XS

List all available models

lemonade list

Hermes Agent

How to use DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-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 DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS

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 DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS

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 "DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF:IQ4_XS" \
  --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"

DavidAU commited on 2 days ago

Commit

7700e17

verified ·

1 Parent(s): c92f1ec

Update README.md

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README.md +9 -6

README.md CHANGED Viewed

@@ -32,7 +32,7 @@ base_model:
 ---
 <SMALL><B><font color="red">IMPORTANT:</font></b> This model has significant internal upgrades under user control.
-Please read this model card carefully to get the most from it - including all the reasoning and instruct modes. The Turbo Brilliance system will begin appearing in other model sizes/types shortly.</small>
 <h2>LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF</h2>
@@ -81,15 +81,18 @@ This is a BETA test ; please report any issues (it worked great // it crashed an
 - Summary of the issue -> positive or negative.
 - Thank you in advance -> this feedback will help refine this systems.
-Limitations:
-- The Brilliance system directly responds to core tuning and fine tuning.
 - The parameters of the model directly impact its power levels, as does the quant used.
-- The larger, more powerful reasoning modes will have the greatest impact.
 - The current reasoning modes (especially) generalists are for all use cases; additional tuning is underway.
 - The current instruct modes (for this specific model) are a wee bit more "verbal" than models with dedicated instruct mode(s).
-- For this specific model: 2.6B (even at SOTA) is still 2.6B parameters.
-Consider this BETA test a window into what models at 4B, 9B, 12B, 27B, 35B (including MOE and sparse moe) and higher can do... because TURBO BRILLIANCE can be used for all them.
 (already in testing/refinement phase)

 ---
 <SMALL><B><font color="red">IMPORTANT:</font></b> This model has significant internal upgrades under user control.
+Please read this model card carefully to get the most from it - including all the reasoning and instruct modes (12+12). The Turbo Brilliance system will begin appearing in other model sizes/types shortly.</small>
 <h2>LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF</h2>
 - Summary of the issue -> positive or negative.
 - Thank you in advance -> this feedback will help refine this systems.
+Limitations - GENERAL:
+- The Brilliance system directly responds to core tuning and fine tuning in the model.
 - The parameters of the model directly impact its power levels, as does the quant used.
+- The larger, more powerful reasoning modes will have the greatest impact on specific use cases.
+Limitations - THIS MODEL:
 - The current reasoning modes (especially) generalists are for all use cases; additional tuning is underway.
 - The current instruct modes (for this specific model) are a wee bit more "verbal" than models with dedicated instruct mode(s).
+- For this specific model: 2.6B (even at SOTA) is still 2.6B parameters ; strongly suggest Q6 or Q8 for best performance.
+Consider this BETA test a window into what models at 4B, 9B, 12B, 27B, 35B (including MOE and sparse moe) and higher can do...
+because TURBO BRILLIANCE can be used for all them.
 (already in testing/refinement phase)