Edit model card

GovLLM Ultra banner

GovLLM-7B-ultra

A question answering model about the Dutch Government.

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

Epoch Loss Grad_norm learning_rate step
0.14 1.3183 0.6038 1.3888e-05 25/540
0.42 1.0220 0.4180 2.8765e-05 75/540
0.69 0.9251 0.4119 2.56793-05 125/540
0.97 0.9260 0.4682 2.2592e-05 175/540
1.25 0.8586 0.5338 1.9506e-05 225/540
1.53 0.8767 0.6359 1.6420e-05 275/540
1.80 0.8721 0.6137 1.3333e-05 325/540
2.08 0.8469 0.7310 1.0247e-05 375/540
2.36 0.8324 0.7945 7.1605e-05 425/540
2.64 0.8170 0.8522 4.0741e-05 475/540
2.91 0.8185 0.8562 9.8765e-05 525/540
Downloads last month
0
Safetensors
Model size
7.24B params
Tensor type
F32
·

Dataset used to train Nelis5174473/GovLLM-7B-ultra