Instructions to use jessitoi/MinistralTrader 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 jessitoi/MinistralTrader 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 jessitoi/MinistralTrader:Q4_K_M # Run inference directly in the terminal: llama cli -hf jessitoi/MinistralTrader:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf jessitoi/MinistralTrader:Q4_K_M # Run inference directly in the terminal: llama cli -hf jessitoi/MinistralTrader:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf jessitoi/MinistralTrader:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf jessitoi/MinistralTrader:Q4_K_M
Use Docker
docker model run hf.co/jessitoi/MinistralTrader:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use jessitoi/MinistralTrader with Ollama:
ollama run hf.co/jessitoi/MinistralTrader:Q4_K_M
- Unsloth Desktop
- Pi
How to use jessitoi/MinistralTrader with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf jessitoi/MinistralTrader:Q4_K_M
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": "jessitoi/MinistralTrader:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use jessitoi/MinistralTrader with Docker Model Runner:
docker model run hf.co/jessitoi/MinistralTrader:Q4_K_M
- Lemonade
How to use jessitoi/MinistralTrader with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jessitoi/MinistralTrader:Q4_K_M
Run and chat with the model
lemonade run user.MinistralTrader-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use jessitoi/MinistralTrader with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf jessitoi/MinistralTrader: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 jessitoi/MinistralTrader:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use jessitoi/MinistralTrader with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf jessitoi/MinistralTrader: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 "jessitoi/MinistralTrader: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"
Download processor_config.json from jessitoi/MinistralTrader: direct link, hf CLI and curl.
- Browser
- Download file 976 Bytes
-
https://huggingface.co/jessitoi/MinistralTrader/resolve/main/processor_config.json
- Command line
-
hf download hf://jessitoi/MinistralTrader/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/jessitoi/MinistralTrader/resolve/main/processor_config.json
976 Bytes
| { | |
| "image_break_token": "[IMG_BREAK]", | |
| "image_end_token": "[IMG_END]", | |
| "image_processor": { | |
| "crop_size": null, | |
| "data_format": "channels_first", | |
| "device": null, | |
| "disable_grouping": null, | |
| "do_center_crop": null, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_pad": null, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "PixtralImageProcessorFast", | |
| "image_seq_length": null, | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "input_data_format": null, | |
| "pad_size": null, | |
| "patch_size": 14, | |
| "processor_class": "PixtralProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_tensors": null, | |
| "size": { | |
| "longest_edge": 1540 | |
| } | |
| }, | |
| "image_token": "[IMG]", | |
| "patch_size": 14, | |
| "processor_class": "PixtralProcessor", | |
| "spatial_merge_size": 2 | |
| } | |