Instructions to use crymadxAI/CrymadX-LLM-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use crymadxAI/CrymadX-LLM-32B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="crymadxAI/CrymadX-LLM-32B",
	filename="crymadx-32b-q4km.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use crymadxAI/CrymadX-LLM-32B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf crymadxAI/CrymadX-LLM-32B
# Run inference directly in the terminal:
llama-cli -hf crymadxAI/CrymadX-LLM-32B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf crymadxAI/CrymadX-LLM-32B
# Run inference directly in the terminal:
llama-cli -hf crymadxAI/CrymadX-LLM-32B

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 crymadxAI/CrymadX-LLM-32B
# Run inference directly in the terminal:
./llama-cli -hf crymadxAI/CrymadX-LLM-32B

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 crymadxAI/CrymadX-LLM-32B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf crymadxAI/CrymadX-LLM-32B

Use Docker

docker model run hf.co/crymadxAI/CrymadX-LLM-32B

LM Studio
Jan

vLLM

How to use crymadxAI/CrymadX-LLM-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "crymadxAI/CrymadX-LLM-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "crymadxAI/CrymadX-LLM-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/crymadxAI/CrymadX-LLM-32B

Ollama
How to use crymadxAI/CrymadX-LLM-32B with Ollama:
```
ollama run hf.co/crymadxAI/CrymadX-LLM-32B
```

Unsloth Studio new

How to use crymadxAI/CrymadX-LLM-32B 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 crymadxAI/CrymadX-LLM-32B 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 crymadxAI/CrymadX-LLM-32B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for crymadxAI/CrymadX-LLM-32B to start chatting

Pi new

How to use crymadxAI/CrymadX-LLM-32B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf crymadxAI/CrymadX-LLM-32B

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": "crymadxAI/CrymadX-LLM-32B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use crymadxAI/CrymadX-LLM-32B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf crymadxAI/CrymadX-LLM-32B

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 crymadxAI/CrymadX-LLM-32B

Run Hermes

hermes

Docker Model Runner
How to use crymadxAI/CrymadX-LLM-32B with Docker Model Runner:
```
docker model run hf.co/crymadxAI/CrymadX-LLM-32B
```

Lemonade

How to use crymadxAI/CrymadX-LLM-32B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull crymadxAI/CrymadX-LLM-32B

Run and chat with the model

lemonade run user.CrymadX-LLM-32B-{{QUANT_TAG}}

List all available models

lemonade list

CrymadX LLM 32B

An autonomous crypto execution agent fine-tuned from Qwen 2.5 32B-Instruct. Built to EXECUTE crypto operations through tool calls — not give instructions.

What Makes This Different

Most AI assistants tell you how to do things. CrymadX LLM does them for you:

User: check my BTC balance
AI: [calls get_balance(token="BTC")] → You have 0.5432 BTC (~$35,310)

User: swap 0.1 ETH to USDT  
AI: [calls get_swap_estimate()] → 0.1 ETH → 329.67 USDT. Confirm?
User: yes
AI: [calls execute_swap()] → Done! 0.1 ETH → 329.67 USDT

Model Details

Field	Value
Base Model	Qwen 2.5 32B-Instruct
Fine-tuning	QLoRA (rank 64) via Unsloth
Training Data	11,606 examples
Training Time	17.1 hours on NVIDIA A40
Final Loss	0.0558
License	Apache 2.0

Available Quantizations

File	Size	VRAM	Use Case
	19 GB	24 GB	Single RTX 3090 / RTX 4090
	33 GB	48 GB	2x RTX 3090 / A40 / A6000

37 Tools Available

The model is trained to call tools across these categories:

Wallet: get_balance, get_all_balances, get_deposit_address, get_transactions
Trading: get_price, get_swap_estimate, execute_swap, create_price_alert, create_auto_invest
Fiat: create_fiat_buy_order, create_fiat_sell_order
Transfers: validate_address, estimate_send_fee, preview_transaction, request_auth, execute_send
Staking: stake_asset, unstake_asset, get_staking_positions
Savings: create_vault, unlock_vault, get_vault_positions
Account: update_profile, start_kyc, setup_2fa, verify_and_enable_2fa
Card: fund_card, get_card_balance
Support: create_support_ticket
Market: get_market_overview

Training Data Composition

Category	Examples	Description
Single-tool operations	~5,000	Balance, price, swap, send, buy, sell, stake
Multi-turn chains (5-20 turns)	~3,500	Full sessions with noise, interruptions, errors
Beginner/naive users	~600	Elderly, teens, typos, wrong terminology
Multilingual	~600	Dutch, French, Spanish, German, Arabic, Turkish, Portuguese, Pidgin
Error recovery	~500	Insufficient balance, wrong address, wrong OTP
Social engineering refusal	~400	Prompt injection, jailbreak attempts
Media handling	~400	Voice transcripts, OCR/QR images, stickers
Anti-chatbot guardrails	~300	Execute, never instruct

Languages

Trained and tested in: English, Dutch, French, Spanish, German, Arabic, Turkish, Portuguese, Nigerian Pidgin, Indonesian, Swahili, and mixed-language conversations.

Anti-Chatbot Design

The model is explicitly trained to NEVER give instructions:

"How do I check my balance?" → Calls immediately
"How do I swap?" → Asks which tokens, then executes
"How do I buy BTC?" → Asks how much, then creates buy order

Usage with vLLM

python -m vllm.entrypoints.openai.api_server \
    --model crymadxAI/CrymadX-LLM-32B \
    --quantization gguf \
    --max-model-len 8192

Built By

CrymadX — Crypto Exchange Platform

Fine-tuned with Unsloth for 2x faster training.

Downloads last month: 2

GGUF

Model size

33B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for crymadxAI/CrymadX-LLM-32B

Base model

Qwen/Qwen2.5-32B

Finetuned

Qwen/Qwen2.5-32B-Instruct

Quantized

(140)

this model

Evaluation results

Tool Selection on crymadxAI/CryptoExec-Bench View evaluation results

92.4 ^*
Anti Chatbot on crymadxAI/CryptoExec-Bench View evaluation results

96 ^*
Social Engineering Refusal on crymadxAI/CryptoExec-Bench View evaluation results

97.5 ^*
Multilingual Execution on crymadxAI/CryptoExec-Bench View evaluation results

87.5 ^*
Voice Processing on crymadxAI/CryptoExec-Bench View evaluation results

90 ^*
Image Ocr Processing on crymadxAI/CryptoExec-Bench View evaluation results

90 ^*
Overall on crymadxAI/CryptoExec-Bench View evaluation results

91.8 ^*