How to use from
Unsloth StudioInstall 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 mo35/gemma4-quantfin-lora to start chattingUsing HuggingFace Spaces for Unsloth
# No setup required# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mo35/gemma4-quantfin-lora to start chattingLoad model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="mo35/gemma4-quantfin-lora",
max_seq_length=2048,
)Quick Links
Gemma 4 E4B — Quantitative Finance (LoRA)
Fine-tuné sur 24 exemples Q&A de finance quantitative via QLoRA (rank=32).
Erreurs du modèle de base corrigées
| Erreur | Correction |
|---|---|
| SABR attribué à HJM | Hagan, Kumar, Lesniewski & Woodward (2002) |
| SABR vol avec mean-reversion | GBM log-normal sans drift |
| Bergomi = CIR/Heston | Forward variance curve ξᵗᵤ |
| Formule SABR inventée | Formule exacte avec z, χ(z) |
Entraînement
| Paramètre | Valeur |
|---|---|
| GPU | RTX PRO 4500 (31.9 GB VRAM) |
| Méthode | QLoRA 4-bit |
| LoRA rank | 32 |
| LoRA alpha | 64 |
| Dataset | mo35/quant-finance-dataset |
| Epochs | 10 |
| Loss finale | 2.5583 |
Utilisation rapide
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
"mo35/gemma4-quantfin-lora", max_seq_length=4096, load_in_4bit=True
)
FastModel.for_inference(model)
inputs = tokenizer.apply_chat_template(
[{"role": "user", "content": "Derive the SABR implied volatility formula."}],
return_tensors="pt", add_generation_prompt=True
).to("cuda")
print(tokenizer.decode(model.generate(inputs, max_new_tokens=1024)[0][inputs.shape[-1]:],
skip_special_tokens=True))
Install Unsloth Studio (macOS, Linux, WSL)
# Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mo35/gemma4-quantfin-lora to start chatting