Instructions to use TheAiCollectiveART/CONSIDER-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use TheAiCollectiveART/CONSIDER-1 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 TheAiCollectiveART/CONSIDER-1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TheAiCollectiveART/CONSIDER-1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TheAiCollectiveART/CONSIDER-1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TheAiCollectiveART/CONSIDER-1:Q4_K_M

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 TheAiCollectiveART/CONSIDER-1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf TheAiCollectiveART/CONSIDER-1:Q4_K_M

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 TheAiCollectiveART/CONSIDER-1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TheAiCollectiveART/CONSIDER-1:Q4_K_M

Use Docker

docker model run hf.co/TheAiCollectiveART/CONSIDER-1:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use TheAiCollectiveART/CONSIDER-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheAiCollectiveART/CONSIDER-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheAiCollectiveART/CONSIDER-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TheAiCollectiveART/CONSIDER-1:Q4_K_M

Ollama
How to use TheAiCollectiveART/CONSIDER-1 with Ollama:
```
ollama run hf.co/TheAiCollectiveART/CONSIDER-1:Q4_K_M
```
Unsloth Desktop

How to use TheAiCollectiveART/CONSIDER-1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TheAiCollectiveART/CONSIDER-1: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": "TheAiCollectiveART/CONSIDER-1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use TheAiCollectiveART/CONSIDER-1 with Docker Model Runner:
```
docker model run hf.co/TheAiCollectiveART/CONSIDER-1:Q4_K_M
```

Lemonade

How to use TheAiCollectiveART/CONSIDER-1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TheAiCollectiveART/CONSIDER-1:Q4_K_M

Run and chat with the model

lemonade run user.CONSIDER-1-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use TheAiCollectiveART/CONSIDER-1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TheAiCollectiveART/CONSIDER-1: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 TheAiCollectiveART/CONSIDER-1:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TheAiCollectiveART/CONSIDER-1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TheAiCollectiveART/CONSIDER-1: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 "TheAiCollectiveART/CONSIDER-1: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"

TheAiCollectiveART commited on Sep 5

Commit

fcd23f4

verified ·

1 Parent(s): d941814

docs: clean typography across all experiments and inventions tables

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -126,7 +126,7 @@ Every generated directive must pass through the in-tree **DCM-ACE** runtime guar
 ### 3. BN254 Zero-Knowledge Nullifiers
 * **Groth16 zk-SNARK Commitments**: Generates cryptographic nullifier hashes over the BN254 (alt_bn128) pairing curve:
   $$\mathcal{H}_{\text{null}} = \text{Poseidon}(\text{Secret}, \text{Nonce}, \text{Epoch})$$
-* **Thermal Noise Shrouding**: Formats spread-spectrum chirp signatures to operate near or below the $-120\text{ dBm}$ thermal noise floor, preventing physical-layer RF fingerprinting and tracking.
 ### 4. Bare-Metal LoRaWAN Hardware Control (RAK Miner SX1302 / SX1303)
 * **Direct SPI Bus Orchestration**: Drives Semtech SX1302/SX1303 concentrators directly via Linux kernel SPI (`/dev/spidev0.0`) at 8 MHz.
@@ -228,7 +228,7 @@ To prove to the open-source and developer community that the entire CONSIDER aut
 | :--- | :--- | :--- | :--- | :--- |
 | **01** | **Full Neural CausalLM Forward Pass** | Executed real PyTorch `AutoModelForCausalLM` loading `models/Qwen3.5-0.8B` (752,393,024 FP32 parameters) on CPU. | Produced output logits shape `[1, 23, 248320]`, top token ID `248068` (`'<think>'`). Verified end-to-end tensor flow. | 🟢 **PASS** |
 | **02** | **Language-U 6D Cuneiform Compression** | Compressed 7,564 bytes of JSON-RPC AST into 3-byte radical `[0x80, 0xF1, 0x0F]`. | **$2,521.3\times$ compression ($99.4\%$)**, 100% lossless roundtrip decoding back to 6D coordinates `[8, 0, 15, 1, 0, 15]`. | 🟢 **PASS** |
-| **03** | **DNA-v2 Dynamic Noise Adaptation** | Telemetry under RSSI **$-124.5\text{ dBm}$**, SNR **$-18.2\text{ dB}$** ($-4.5\text{ dB}$ below thermal noise). | Voronoi expansion parameter $\Delta\tau = 1.6038$, Shannon entropy $\mathcal{H} = 2.000\text{ bits}$, **zero bit flips**. | 🟢 **PASS** |
 | **04** | **BN254 Groth16 Zero-Knowledge Nullifiers** | Generated 1,000 epoch nonces modulo scalar field $r$ under Poseidon hash simulation. | 1,000 / 1,000 distinct valid field elements, **0 collisions**. | 🟢 **PASS** |
 | **05** | **Recursive ZK-Nullifier Proof Folding** | Folded 50 edge node nullifiers into a single 64-byte aggregated signature accumulator. | Slices on-chain transaction footprint by **$50\times$** at constant **150 Compute Units**. | 🟢 **PASS** |
 | **06** | **DCM-ACE Runtime Metacognitive Healing** | Injected 6 hardware hallucinations (wrong GPIO pin, wrong SPI bus, excessive clock, out-of-band RF frequency, invalid SF, high TX power). | 6 / 6 anomalies intercepted and healed deterministically in **$55.50\ \mu\text{s}$**. | 🟢 **PASS** |

 ### 3. BN254 Zero-Knowledge Nullifiers
 * **Groth16 zk-SNARK Commitments**: Generates cryptographic nullifier hashes over the BN254 (alt_bn128) pairing curve:
   $$\mathcal{H}_{\text{null}} = \text{Poseidon}(\text{Secret}, \text{Nonce}, \text{Epoch})$$
+* **Thermal Noise Shrouding**: Formats spread-spectrum chirp signatures to operate near or below the -120 dBm thermal noise floor, preventing physical-layer RF fingerprinting and tracking.
 ### 4. Bare-Metal LoRaWAN Hardware Control (RAK Miner SX1302 / SX1303)
 * **Direct SPI Bus Orchestration**: Drives Semtech SX1302/SX1303 concentrators directly via Linux kernel SPI (`/dev/spidev0.0`) at 8 MHz.
 | :--- | :--- | :--- | :--- | :--- |
 | **01** | **Full Neural CausalLM Forward Pass** | Executed real PyTorch `AutoModelForCausalLM` loading `models/Qwen3.5-0.8B` (752,393,024 FP32 parameters) on CPU. | Produced output logits shape `[1, 23, 248320]`, top token ID `248068` (`'<think>'`). Verified end-to-end tensor flow. | 🟢 **PASS** |
 | **02** | **Language-U 6D Cuneiform Compression** | Compressed 7,564 bytes of JSON-RPC AST into 3-byte radical `[0x80, 0xF1, 0x0F]`. | **$2,521.3\times$ compression ($99.4\%$)**, 100% lossless roundtrip decoding back to 6D coordinates `[8, 0, 15, 1, 0, 15]`. | 🟢 **PASS** |
+| **03** | **DNA-v2 Dynamic Noise Adaptation** | Telemetry under RSSI **-124.5 dBm**, SNR **-18.2 dB** (-4.5 dB below thermal noise). | Voronoi expansion parameter Δτ = 1.6038, Shannon entropy ℋ = 2.000 bits, **zero bit flips**. | 🟢 **PASS** |
 | **04** | **BN254 Groth16 Zero-Knowledge Nullifiers** | Generated 1,000 epoch nonces modulo scalar field $r$ under Poseidon hash simulation. | 1,000 / 1,000 distinct valid field elements, **0 collisions**. | 🟢 **PASS** |
 | **05** | **Recursive ZK-Nullifier Proof Folding** | Folded 50 edge node nullifiers into a single 64-byte aggregated signature accumulator. | Slices on-chain transaction footprint by **$50\times$** at constant **150 Compute Units**. | 🟢 **PASS** |
 | **06** | **DCM-ACE Runtime Metacognitive Healing** | Injected 6 hardware hallucinations (wrong GPIO pin, wrong SPI bus, excessive clock, out-of-band RF frequency, invalid SF, high TX power). | 6 / 6 anomalies intercepted and healed deterministically in **$55.50\ \mu\text{s}$**. | 🟢 **PASS** |