Instructions to use deadbydawn101/RavenX-Trade-8B-MAP-128k-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deadbydawn101/RavenX-Trade-8B-MAP-128k-mlx-4bit with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("deadbydawn101/RavenX-Trade-8B-MAP-128k-mlx-4bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use deadbydawn101/RavenX-Trade-8B-MAP-128k-mlx-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "deadbydawn101/RavenX-Trade-8B-MAP-128k-mlx-4bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "deadbydawn101/RavenX-Trade-8B-MAP-128k-mlx-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use deadbydawn101/RavenX-Trade-8B-MAP-128k-mlx-4bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "deadbydawn101/RavenX-Trade-8B-MAP-128k-mlx-4bit"

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-MAP-128k-mlx-4bit

Run Hermes

hermes

MLX LM

How to use deadbydawn101/RavenX-Trade-8B-MAP-128k-mlx-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "deadbydawn101/RavenX-Trade-8B-MAP-128k-mlx-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "deadbydawn101/RavenX-Trade-8B-MAP-128k-mlx-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "deadbydawn101/RavenX-Trade-8B-MAP-128k-mlx-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

🐦‍⬛ RavenX-Trade 8B v1.1 — Autonomous Trading Intelligence

MLX 4-bit · Apple Silicon · 128K context · 4-step MAP protocol · 318K training examples

Qwen3-8B fine-tuned for autonomous trading analysis. Trained on 318,675 examples from Polymarket, crypto, stocks, options, hedge fund, and quantitative trading data. Follow the MAP protocol: Market → Analyze → Predict → Trade.

📦 GGUF (Ollama / llama.cpp): RavenX-Trade-8B-GGUF

🛡️ Security model: RavenX-Sec-8B-RATH-128k (610K examples, 6-step RATH protocol)

Built by @DeadByDawn101 · RavenX LLC

What This Model Does

A fine-tuned Qwen3-8B that follows the MAP protocol for every trading analysis:

MAP Step	What It Does
M — Market	Identifies asset, price, timeframe, market context
A — Analyze	Technical indicators, volume, momentum, on-chain signals
P — Predict	Signal direction, confidence level, target price
T — Trade	Entry, stop loss, target, risk/reward, position sizing

Example Output

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

Quick Start (MLX)

from mlx_lm import load, generate

model, tokenizer = load("deadbydawn101/RavenX-Trade-8B-MAP-128k-mlx-4bit")

messages = [
    {"role": "system", "content": "You are RavenX-Trade. Follow the MAP protocol (Market, Analyze, Predict, Trade) for every analysis."},
    {"role": "user", "content": "BTC at 67500. RSI 28. MACD bullish crossover. Volume 3x average. Analyze."}
]

prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
response = generate(model, tokenizer, prompt=prompt, max_tokens=2048)
print(response)

Chat REPL

python3 -m mlx_lm chat --model deadbydawn101/RavenX-Trade-8B-MAP-128k-mlx-4bit --max-tokens 2048

OpenAI-Compatible Server

python3 -m mlx_lm.server --model deadbydawn101/RavenX-Trade-8B-MAP-128k-mlx-4bit

Training Details

Parameter	Value
Architecture	Qwen3-8B (Josiefied abliterated)
Training Data	318,675 examples
Context Window	128K (YaRN rope scaling, factor 4.0)
Method	MLX LoRA (rank 32, 8 layers)
Learning Rate	1e-5
Batch Size	2
Iterations	2,000
Sequence Length	8,192
Hardware	Apple M1 Max 64GB

Dataset Composition (318K examples)

Category	Examples	Weight
Polymarket	86,800	2x
Stock/Options Trading	36,000	2x
Hedge Fund Strategies	14,200	2x
Crypto Trading	7,100	2x
Agent/Tool Calling	16,100	1x
GitHub Repos (15)	3,422	3x
Synthetic MAP	custom	2x

Datasets Used (21)

Polymarket: polymarket-quant-bench (36.8K) · polymarket-transactions (50K streamed)

Crypto: Solana-blockchain-360-Coding · crypto-agent-safe-function-calling · crypto-trading-r1-rl · crypto-ai-trading-signals

Stocks/Quant: stock-trader-sft-v2 (36K) · QuantitativeEquityInvesting · stock-market-chart-patterns

Hedge Fund: Hedgefunddataset (14.2K)

Agent/Reasoning: Claude-TraceInversion · ToolACE

15 GitHub Repos: polyverse · polymarket-trade-engine · tradingagents · ai-hedge-fund · MiroFish · Polymarket-BTC-15-Minute-Trading-Bot · pump-fun-bundler-bot · pumpfun-mcp-server · auto-researchtrading · degen-financial-plugins · polyterm · gopher · trading-bot · open-reasoning · minimind