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-GGUF / README.md

deadbydawn101

v1.1: Model card — 318K examples, MAP protocol, 128K context

cd527f3 verified 2 months ago

preview code

Raw

History Blame Contribute Delete

3.14 kB

	---
	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](https://huggingface.co/deadbydawn101/RavenX-Trade-8B-MAP-128k-mlx-4bit)
	>
	> 🛡️ Security model: [RavenX-Sec-8B-GGUF](https://huggingface.co/deadbydawn101/RavenX-Sec-8B-GGUF)

	Built by [@DeadByDawn101](https://github.com/DeadByDawn101) · RavenX LLC

	---

	## Quick Start (Ollama)

	```bash
	# 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](https://huggingface.co/deadbydawn101/RavenX-Sec-8B-GGUF) \| Security \| 6-step RATH \| 610K examples \|
	\| [RavenX-Trade v1.1](https://huggingface.co/deadbydawn101/RavenX-Trade-8B-GGUF) \| Trading \| 4-step MAP \| 318K examples \|

	---

	## Source Code

	[github.com/DeadByDawn101/RavenX-Trade](https://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