Instructions to use gptradeinvest/sigmaquant-copilot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gptradeinvest/sigmaquant-copilot with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="gptradeinvest/sigmaquant-copilot",
	filename="sqsl-2.0-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use gptradeinvest/sigmaquant-copilot 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 gptradeinvest/sigmaquant-copilot:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf gptradeinvest/sigmaquant-copilot:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf gptradeinvest/sigmaquant-copilot:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf gptradeinvest/sigmaquant-copilot: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 gptradeinvest/sigmaquant-copilot:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf gptradeinvest/sigmaquant-copilot: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 gptradeinvest/sigmaquant-copilot:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf gptradeinvest/sigmaquant-copilot:Q4_K_M

Use Docker

docker model run hf.co/gptradeinvest/sigmaquant-copilot:Q4_K_M

LM Studio
Jan

vLLM

How to use gptradeinvest/sigmaquant-copilot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gptradeinvest/sigmaquant-copilot"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gptradeinvest/sigmaquant-copilot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gptradeinvest/sigmaquant-copilot:Q4_K_M

Ollama
How to use gptradeinvest/sigmaquant-copilot with Ollama:
```
ollama run hf.co/gptradeinvest/sigmaquant-copilot:Q4_K_M
```

Unsloth Studio

How to use gptradeinvest/sigmaquant-copilot 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 gptradeinvest/sigmaquant-copilot 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 gptradeinvest/sigmaquant-copilot to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for gptradeinvest/sigmaquant-copilot to start chatting

Atomic Chat new
Docker Model Runner
How to use gptradeinvest/sigmaquant-copilot with Docker Model Runner:
```
docker model run hf.co/gptradeinvest/sigmaquant-copilot:Q4_K_M
```

Lemonade

How to use gptradeinvest/sigmaquant-copilot with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull gptradeinvest/sigmaquant-copilot:Q4_K_M

Run and chat with the model

lemonade run user.sigmaquant-copilot-Q4_K_M

List all available models

lemonade list

SigmaQuant Copilot — SQSL 2.0 (GGUF)

Modèle de copilote quantitatif en français (~4 milliards de paramètres), quantifié Q4_K_M pour llama.cpp. Il route chaque question vers la bonne méthode financière et délègue tout calcul exact à un moteur déterministe via un bloc ```engine — il ne fait jamais d'arithmétique lui-même, donc les nombres renvoyés par l'application sont exacts et auditables.

Fichier : sqsl-2.0-Q4_K_M.gguf
Format : GGUF (quantification Q4_K_M)
Runtime : llama-server (llama.cpp), template de chat ChatML
App de référence : https://github.com/Gaetan-PRUVOT-SQS/sigmaquant-copilot (app macOS native SwiftUI, 100 % locale)

Utilisation (llama.cpp)

hf download gptradeinvest/sigmaquant-copilot sqsl-2.0-Q4_K_M.gguf --local-dir models
llama-server -m models/sqsl-2.0-Q4_K_M.gguf --chat-template chatml -c 4096 -ngl 999

Envoyez toujours le prompt système fourni avec l'application. Le modèle émet un bloc ```engine {module, function, params} que le moteur déterministe exécute pour produire le nombre final.

Modules couverts

01 — Fondations : Black-Scholes, parité put-call, forwards, binomial risque-neutre, rente perpétuelle
02 — Crédit & structure par terme : spread CDS au pair, treillis ZC, crédit amortissable, hasard/survie
03 — Portefeuille & exécution : MEDAF, Sharpe, exécution optimale Almgren-Chriss
04 — Pricing avancé : grecques (delta), densité de Breeden-Litzenberger, perte de tranche CDO
05 — Calcul : pricing FFT Carr-Madan, calibration de modèles, ajustement Vasicek

Licence

Downloads last month: 38

GGUF

Model size

4B params

Architecture

qwen35

Hardware compatibility

4-bit