GovLLM-7B-ultra

<p align="center" style="margin:0;padding:0"> <img src="https://cdn-uploads.huggingface.co/production/uploads/65e04544f59f66e0e072dc5c/b-OsZLNJtPHMwzbgwmGlV.png" alt="GovLLM Ultra banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> </p> <div style="margin:auto; text-align:center"> <h1 style="margin-bottom: 0">GovLLM-7B-ultra</h1> <em>A question answering model about the Dutch Government.</em> </div>

Model description

This model is a fine-tuned version of the Dutch conversational model BramVanroy/GEITje-7B-ULTRA on a Dutch question-answer pair dataset of the Dutch Government. This is a Dutch question/answer model ultimately based on Mistral and fine-tuned with SFT and LoRA. The training with 3 epochs took almost 2 hours and was run on an Nvidia A100 (40GB VRAM).

Usage with Inference Endpoints (Dedicated)

import requests

API_URL = "https://your-own-endpoint.us-east-1.aws.endpoints.huggingface.cloud"
headers = {"Authorization": "Bearer hf_your_own_token"}

def query(payload):
	response = requests.post(API_URL, headers=headers, json=payload)
	return response.json()

output = query({
	"inputs": "Geeft de overheid subsidie aan bedrijven?"
})

# print generated answer
print(output[0]['generated_text'])

Training hyperparameters

The following hyperparameters were used during training:

• block_size: 1024,
• model_max_length: 2048,
• padding: right,
• mixed_precision: fp16,
• learning rate (lr): 0.00003,
• epochs: 3,
• batch_size: 2,
• optimizer: adamw_torch,
• schedular: linear,
• quantization: int8,
• peft: true,
• lora_r: 16,
• lora_alpha: 16,
• lora_dropout: 0.05

Training results