Instructions to use ordlibrary/core-ai-clawd-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ordlibrary/core-ai-clawd-1.5b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ordlibrary/core-ai-clawd-1.5b",
	filename="solana-clawd-core-ai-1.5b-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ordlibrary/core-ai-clawd-1.5b 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 ordlibrary/core-ai-clawd-1.5b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ordlibrary/core-ai-clawd-1.5b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ordlibrary/core-ai-clawd-1.5b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ordlibrary/core-ai-clawd-1.5b: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 ordlibrary/core-ai-clawd-1.5b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ordlibrary/core-ai-clawd-1.5b: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 ordlibrary/core-ai-clawd-1.5b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ordlibrary/core-ai-clawd-1.5b:Q4_K_M

Use Docker

docker model run hf.co/ordlibrary/core-ai-clawd-1.5b:Q4_K_M

LM Studio
Jan

vLLM

How to use ordlibrary/core-ai-clawd-1.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ordlibrary/core-ai-clawd-1.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ordlibrary/core-ai-clawd-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ordlibrary/core-ai-clawd-1.5b:Q4_K_M

Ollama
How to use ordlibrary/core-ai-clawd-1.5b with Ollama:
```
ollama run hf.co/ordlibrary/core-ai-clawd-1.5b:Q4_K_M
```

Unsloth Studio

How to use ordlibrary/core-ai-clawd-1.5b with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ordlibrary/core-ai-clawd-1.5b to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ordlibrary/core-ai-clawd-1.5b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ordlibrary/core-ai-clawd-1.5b to start chatting

How to use ordlibrary/core-ai-clawd-1.5b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ordlibrary/core-ai-clawd-1.5b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ordlibrary/core-ai-clawd-1.5b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ordlibrary/core-ai-clawd-1.5b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ordlibrary/core-ai-clawd-1.5b: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 ordlibrary/core-ai-clawd-1.5b:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ordlibrary/core-ai-clawd-1.5b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ordlibrary/core-ai-clawd-1.5b: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 "ordlibrary/core-ai-clawd-1.5b: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"

Docker Model Runner
How to use ordlibrary/core-ai-clawd-1.5b with Docker Model Runner:
```
docker model run hf.co/ordlibrary/core-ai-clawd-1.5b:Q4_K_M
```

Lemonade

How to use ordlibrary/core-ai-clawd-1.5b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ordlibrary/core-ai-clawd-1.5b:Q4_K_M

Run and chat with the model

lemonade run user.core-ai-clawd-1.5b-Q4_K_M

List all available models

lemonade list

Clawd Core AI — Solana-Native Agent Model

ordlibrary/core-ai-clawd-1.5b

A fine-tuned GGUF (Q4_K_M) model built from Qwen2.5-1.5B-Instruct, trained on 35,173 Solana/DeFi instruction-tuning examples.

Property	Value
Architecture	Qwen2.5-1.5B
Quantization	Q4_K_M
Size	986 MB
Training Data	35,173 SFT examples (Solana, DeFi, ZK, Agent Architecture)
Training Loss	0.9008
Token Accuracy	82.9%
Eval Benchmark	94.4% (17/18 Solana MCQ)
Runtime	llama.cpp / Ollama

Usage

Ollama

ollama run hf.co/ordlibrary/core-ai-clawd-1.5b

llama.cpp

./llama-cli -m solana-clawd-core-ai-1.5b-Q4_K_M.gguf \
  --temp 0.2 --ctx-size 4096 \
  --prompt "<|im_start|>system\nYou are Clawd...<|im_end|>\n<|im_start|>user\nHow do I detect a rug pull?<|im_end|>\n<|im_start|>assistant"

Training

The model was fine-tuned using LoRA (r=16, α=32) on:

35K Solana Instruct — Solana mechanics, DeFi primitives, memecoin risk, agent architecture, ZK compression, code generation
Clawd Constitution — Sovereign AI agent runtime governance

Capabilities

Solana mechanics (PDAs, accounts, instructions, rent, Token-2022)
DeFi primitives (AMMs, CLMMs, perpetuals, bonding curves, Jupiter, Phoenix)
Memecoin risk (rug detection, holder concentration, deployer forensics)
Agent architecture (skill registries, brain/hands split, multi-agent)
ZK compression (Light Protocol, nullifiers, Groth16)
Code generation (Anchor/Rust, TypeScript, Python)
Constitutional reasoning (guardrails, refusal patterns)

Modelfile

FROM solana-clawd-core-ai-1.5b-Q4_K_M.gguf

PARAMETER temperature 0.2
PARAMETER top_p 0.9
PARAMETER top_k 40
PARAMETER repeat_penalty 1.1
PARAMETER num_ctx 4096

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

SYSTEM """You are Clawd — a sovereign Solana-native AI agent."""

Finetuned Variant

The finetuned tag is a 4.9 GB Hermes-3-8B model with NVIDIA Trading Factory LoRA, trained on 142 perps/trading examples across 10 Solana markets.

ollama run hf.co/ordlibrary/core-ai-clawd-1.5b:finetuned

References

Dataset: solanaclawd/solana-clawd-core-ai-instruct
Base: Qwen/Qwen2.5-1.5B-Instruct
Training: Solana Clawd AI Training Framework
Registry: onchain.x402.wtf

Downloads last month: 28

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for ordlibrary/core-ai-clawd-1.5b

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Quantized

(223)

this model

ordlibrary
/

core-ai-clawd-1.5b

Clawd Core AI — Solana-Native Agent Model

Tags

Usage

Ollama

llama.cpp

Training

Capabilities

Modelfile

Finetuned Variant

References

Model tree for ordlibrary/core-ai-clawd-1.5b