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

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

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

🐦‍⬛ 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

Downloads last month: 226

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Model tree for deadbydawn101/RavenX-Trade-8B-GGUF

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Finetuned

Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1

Quantized

(26)

this model