Text Generation
PEFT
Safetensors
English
trading
finance
hyperliquid
perpetuals
defi
lora
dpo
sft
trl
conversational
Instructions to use UVLabs/HyperLLM-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use UVLabs/HyperLLM-4b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507") model = PeftModel.from_pretrained(base_model, "UVLabs/HyperLLM-4b") - Notebooks
- Google Colab
- Kaggle
v0.6: +15.2% accuracy with robust extraction (90.2% overall)
Browse filesKey improvements over v0.4:
- Adversarial %: 71% → 93% (+22%)
- Multi-step: 32% → 92.3% (+60.3%)
- Position Sizing: 81.7% → 98.3% (+16.6%)
- Overall: 75% → 90.2% (+15.2%)
Fixed evaluation extraction bugs that caused 17% false negative rate.
adapter_model.safetensors
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