Instructions to use GoshawkVortexAI/Goshawk_Fingpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use GoshawkVortexAI/Goshawk_Fingpt 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 GoshawkVortexAI/Goshawk_Fingpt:Q8_0
# Run inference directly in the terminal:
llama cli -hf GoshawkVortexAI/Goshawk_Fingpt:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf GoshawkVortexAI/Goshawk_Fingpt:Q8_0
# Run inference directly in the terminal:
llama cli -hf GoshawkVortexAI/Goshawk_Fingpt:Q8_0

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 GoshawkVortexAI/Goshawk_Fingpt:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf GoshawkVortexAI/Goshawk_Fingpt:Q8_0

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 GoshawkVortexAI/Goshawk_Fingpt:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GoshawkVortexAI/Goshawk_Fingpt:Q8_0

Use Docker

docker model run hf.co/GoshawkVortexAI/Goshawk_Fingpt:Q8_0

LM Studio
Jan
Ollama
How to use GoshawkVortexAI/Goshawk_Fingpt with Ollama:
```
ollama run hf.co/GoshawkVortexAI/Goshawk_Fingpt:Q8_0
```
Unsloth Desktop

How to use GoshawkVortexAI/Goshawk_Fingpt with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GoshawkVortexAI/Goshawk_Fingpt:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "GoshawkVortexAI/Goshawk_Fingpt:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use GoshawkVortexAI/Goshawk_Fingpt with Docker Model Runner:
```
docker model run hf.co/GoshawkVortexAI/Goshawk_Fingpt:Q8_0
```

Lemonade

How to use GoshawkVortexAI/Goshawk_Fingpt with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GoshawkVortexAI/Goshawk_Fingpt:Q8_0

Run and chat with the model

lemonade run user.Goshawk_Fingpt-Q8_0

List all available models

lemonade list

Hermes Agent

How to use GoshawkVortexAI/Goshawk_Fingpt with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GoshawkVortexAI/Goshawk_Fingpt:Q8_0

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 GoshawkVortexAI/Goshawk_Fingpt:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use GoshawkVortexAI/Goshawk_Fingpt with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GoshawkVortexAI/Goshawk_Fingpt:Q8_0

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 "GoshawkVortexAI/Goshawk_Fingpt:Q8_0" \
  --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"

FinGPT-Crypto v5

LoRA fine-tune of Qwen3-8B for structured crypto trading verdict generation, trained on Apple Silicon via MLX-LM.

Given a news headline plus market context (price, funding rate, volatility regime, trend, BTC alignment, etc.), the model outputs a structured JSON verdict:

{
  "refined_conviction": 0.74,
  "conviction_adjustment": -0.04,
  "reasoning_summary": "Funding building positive, no negative catalysts in 24h, BTC NORMAL_STABLE.",
  "concerns": ["ls_extremes_approaching_crowded_60pct"],
  "supportive_factors": ["funding_curve_steepening", "no_event_risk_in_horizon"],
  "would_recommend_skip": false,
  "confidence": 0.82,
  "horizons": {
    "1h":  {"direction": "LONG", "confidence": 75},
    "4h":  {"direction": "LONG", "confidence": 70},
    "24h": {"direction": "SKIP", "confidence": 60}
  }
}

Files

fingpt-crypto-v5-full-test.q8_0.gguf — Q8_0 quantized GGUF (8.7GB), for use with Ollama or llama.cpp.

Training

Base: Qwen3-8B, 4-bit quantized during training (MLX)
Method: LoRA (rank 8, scale 20), resumed incrementally across v1→v5, 16 of 36 layers
v5 stage: 5000 iterations, batch size 1, lr 1e-5, max_seq_length 1024
Final val loss: 0.311

Usage (Ollama)

ollama create fingpt-crypto:v5 -f Modelfile
ollama run fingpt-crypto:v5

Modelfile:

FROM fingpt-crypto-v5-full-test.q8_0.gguf
PARAMETER temperature 0.1
PARAMETER top_p 0.9
PARAMETER num_ctx 4096

Notes

This is a research/experimental fine-tune for a personal crypto trading project. Not financial advice. Use at your own risk.

Downloads last month: 10

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

8-bit

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