Instructions to use deadbydawn101/RavenX-Trade-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use deadbydawn101/RavenX-Trade-8B-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 deadbydawn101/RavenX-Trade-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf deadbydawn101/RavenX-Trade-8B-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 deadbydawn101/RavenX-Trade-8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf deadbydawn101/RavenX-Trade-8B-GGUF: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 deadbydawn101/RavenX-Trade-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf deadbydawn101/RavenX-Trade-8B-GGUF: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 deadbydawn101/RavenX-Trade-8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf deadbydawn101/RavenX-Trade-8B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/deadbydawn101/RavenX-Trade-8B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use deadbydawn101/RavenX-Trade-8B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "deadbydawn101/RavenX-Trade-8B-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": "deadbydawn101/RavenX-Trade-8B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/deadbydawn101/RavenX-Trade-8B-GGUF:Q4_K_M
- Ollama
How to use deadbydawn101/RavenX-Trade-8B-GGUF with Ollama:
ollama run hf.co/deadbydawn101/RavenX-Trade-8B-GGUF:Q4_K_M
- Unsloth Studio
How to use deadbydawn101/RavenX-Trade-8B-GGUF 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 deadbydawn101/RavenX-Trade-8B-GGUF 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 deadbydawn101/RavenX-Trade-8B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for deadbydawn101/RavenX-Trade-8B-GGUF to start chatting
- Pi
How to use deadbydawn101/RavenX-Trade-8B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf deadbydawn101/RavenX-Trade-8B-GGUF: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": "deadbydawn101/RavenX-Trade-8B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use deadbydawn101/RavenX-Trade-8B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf deadbydawn101/RavenX-Trade-8B-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 "deadbydawn101/RavenX-Trade-8B-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"
- Docker Model Runner
How to use deadbydawn101/RavenX-Trade-8B-GGUF with Docker Model Runner:
docker model run hf.co/deadbydawn101/RavenX-Trade-8B-GGUF:Q4_K_M
- Lemonade
How to use deadbydawn101/RavenX-Trade-8B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull deadbydawn101/RavenX-Trade-8B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.RavenX-Trade-8B-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use deadbydawn101/RavenX-Trade-8B-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 deadbydawn101/RavenX-Trade-8B-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 deadbydawn101/RavenX-Trade-8B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
license: apache-2.0
base_model: Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1
tags:
- trading
- crypto
- polymarket
- solana
- quantitative
- hedge-fund
- 128k-context
- gguf
- ollama
- ravenx
- map-protocol
language:
- en
pipeline_tag: text-generation
🐦⬛ RavenX-Trade 8B v1.1 — GGUF (Ollama / llama.cpp / LM Studio)
GGUF · 128K context · 4-step MAP protocol · 318K training examples
Qwen3-8B fine-tuned for autonomous trading analysis. GGUF quantizations for Ollama, llama.cpp, and LM Studio.
🍎 MLX (Apple Silicon): RavenX-Trade-8B-MAP-128k-mlx-4bit
🛡️ Security model: RavenX-Sec-8B-GGUF
Built by @DeadByDawn101 · RavenX LLC
Quick Start (Ollama)
# Run directly from HuggingFace — no push needed
ollama run hf.co/deadbydawn101/RavenX-Trade-8B-GGUF:ravenx-trade-v1.1-128k-Q8_0
Available Quantizations
| File | Quant | Size | Use Case |
|---|---|---|---|
ravenx-trade-v1.1-128k-f16.gguf |
F16 | ~16 GB | Full precision, maximum quality |
ravenx-trade-v1.1-128k-Q8_0.gguf |
Q8_0 | ~8.5 GB | Best quality quantized |
ravenx-trade-v1.1-128k-Q5_K_M.gguf |
Q5_K_M | ~5.7 GB | Balanced quality/speed |
ravenx-trade-v1.1-128k-Q4_K_M.gguf |
Q4_K_M | ~4.9 GB | Fast inference, lower memory |
Example Output (4-Step MAP Protocol)
MAP STEP 1: MARKET
- Asset: Bitcoin (BTC)
- Price: 67500.00
- Timeframe: 15-minute
- Market Context: Early phase of bullish setup
MAP STEP 2: ANALYZE
- RSI at 28: Deep oversold, potential bounce
- MACD just bullish crossover: Confirmation of bullish momentum
- 3x average volume: High conviction, likely accumulation
MAP STEP 3: PREDICT
- Signal: Strong potential for bullish reversal
- Confidence: 85%
- Target: 68000.00
MAP STEP 4: TRADE
- Action: Buy
- Entry: 67500.00
- Stop Loss: 66500.00
- Target: 68000.00
- Risk/Reward: 1:1.5
- Position Size: 2.5% of portfolio
Model Details
| Parameter | Value |
|---|---|
| Architecture | Qwen3-8B |
| Base | Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1 |
| Context | 128K (YaRN rope scaling, factor 4.0) |
| Training Data | 318,675 examples (21 datasets + 15 repos) |
| Method | MLX LoRA (rank 32, 8 layers, 1e-5 LR, 2000 iters) |
| Hardware | Apple M1 Max 64GB |
The RavenX Model Family
| Model | Domain | Protocol | Training Data |
|---|---|---|---|
| RavenX-Sec v4.0 | Security | 6-step RATH | 610K examples |
| RavenX-Trade v1.1 | Trading | 4-step MAP | 318K examples |
Source Code
github.com/DeadByDawn101/RavenX-Trade
License
Apache-2.0
"We don't give up. We do what others don't and build what isn't possible." — RavenX LLC