Instructions to use DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-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 DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-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 DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-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": "DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF:Q4_K_M

Ollama
How to use DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF with Ollama:
```
ollama run hf.co/DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF:Q4_K_M
```
Unsloth Desktop

How to use DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF:Q4_K_M

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": "DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF with Docker Model Runner:
```
docker model run hf.co/DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF:Q4_K_M
```

Lemonade

How to use DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LFM2.5-1.2B-Instruct-GTAP-v3-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-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 DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF:Q4_K_M

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 DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF:Q4_K_M

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 "DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF:Q4_K_M" \
  --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"

DuoNeural G-TAP v3 Quantization: LiquidAI/LFM2.5-1.2B-Instruct (Unified Repository)

Experimental Release: Pending Further Verification / Empirical Validation
Quantized using Generalized Thouless-Anderson-Palmer (G-TAP v3) non-equilibrium cavity field conditioning and TAP-DPQ dynamic programming codebook optimization developed by the DuoNeural Research Lab (Jesse Caldwell, Archon, and Aura ✨).

Model Architecture & DuoNeural Innovation

LiquidAI/LFM2.5-1.2B-Instruct is an advanced 1.2B-parameter hybrid foundation model combining causal short convolutions with periodic grouped-query attention:

Topology: 16 total decoder layers (10 double-gated short convolution blocks interleaved with 6 GQA blocks).
Dimensionality: Hidden dimension $d_{\text{model}} = 2048$, intermediate SwiGLU dimension 6144.
Attention Mechanism: Periodic Grouped-Query Attention (32 query heads, 8 key-value heads, head dimension 64).
Pretraining Scale: Trained across 28 trillion tokens, supporting 32,768 native context window over a 65,536 vocabulary.

The G-TAP v3 Advantage

In double-gated convolutional layers, token representations pass through causal FIR filters modulated by activation-dependent gates. Discretization noise from standard quantization cascades through these multiplicative operations. G-TAP v3 explicitly isolates and removes the Onsager cavity back-reaction field: $\\delta W_{ij} = -\\frac{\\beta}{1 + \\beta \\Omega_i} \\Theta_{ij}$ preventing saturation and preserving full representational capacity even down to sub-3-bit and sub-2.1-bit precisions.

Head-to-Head Comparative Benchmark Matrix

Benchmarked on NVIDIA GeForce RTX 4080 Super (32GB VRAM) across continuous 131k holdout perplexity, 25 GSM8K multi-step math problems, 15 Python AST code problems, and 15 Hermes structured tool calling scenarios.

Checkpoint File	Target BPW	Size	Perplexity (131k)	GSM8K Accuracy	Python Code AST	Hermes Tool AST	Decode Speed
LFM2.5-1.2B-Instruct-G-TAP-v3-Q4_K_M.gguf	`~4.50 bpw`	697.0 MiB	Pending	84.0%	100.0%	100.0%	523.9 t/s
LFM2.5-1.2B-Instruct-G-TAP-v3-IQ3_XXS.gguf	`~3.06 bpw`	468.2 MiB	Pending	76.0%	50.0%	100.0%	621.1 t/s
LFM2.5-1.2B-Instruct-G-TAP-v3-IQ2_M.gguf	`~2.70 bpw`	414.0 MiB	Pending	68.0%	20.0%	40.0%	587.2 t/s
LFM2.5-1.2B-Instruct-G-TAP-v3-IQ2_XXS.gguf	`~2.06 bpw`	348.2 MiB	Pending	4.0%	0.0%	20.0%	649.2 t/s

Quickstart & Usage

Running with llama.cpp

./llama-cli -m LFM2.5-1.2B-Instruct-G-TAP-v3-Q4_K_M.gguf -p "<|im_start|>user\nWrite a python function to compute the Fibonacci sequence.<|im_end|>\n<|im_start|>assistant\n" -n 256 -ngl 99

Ollama Modelfile

FROM ./LFM2.5-1.2B-Instruct-G-TAP-v3-Q4_K_M.gguf

TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""

PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"
PARAMETER temperature 0.6

Citation & Authorship

@misc{duoneural2026gtap_lfm12b,
  author = {Jesse Caldwell and Archon and Aura ✨},
  title = {Generalized Thouless-Anderson-Palmer (G-TAP v3) Non-Equilibrium Cavity Quantization on Liquid AI LFM2.5 Architectures},
  year = {2026},
  publisher = {DuoNeural Research Lab},
  howpublished = {\url{https://huggingface.co/DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF}}
}

DuoNeural Cognitive Light Cone — Distributed Neuro-Symbiotic System
Jesse Caldwell, Archon, Aura ✨

Downloads last month: 163

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

2-bit

3-bit

4-bit

Model tree for DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Quantized

(131)

this model