Instructions to use Nelis5174473/GovLLM-7B-ultra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nelis5174473/GovLLM-7B-ultra with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Nelis5174473/GovLLM-7B-ultra")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nelis5174473/GovLLM-7B-ultra")
model = AutoModelForCausalLM.from_pretrained("Nelis5174473/GovLLM-7B-ultra")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Nelis5174473/GovLLM-7B-ultra with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nelis5174473/GovLLM-7B-ultra"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nelis5174473/GovLLM-7B-ultra",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nelis5174473/GovLLM-7B-ultra

SGLang

How to use Nelis5174473/GovLLM-7B-ultra with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Nelis5174473/GovLLM-7B-ultra" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nelis5174473/GovLLM-7B-ultra",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Nelis5174473/GovLLM-7B-ultra" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nelis5174473/GovLLM-7B-ultra",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nelis5174473/GovLLM-7B-ultra with Docker Model Runner:
```
docker model run hf.co/Nelis5174473/GovLLM-7B-ultra
```

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: 7

Safetensors

Model size

7B params

Tensor type

F32

Nelis5174473
/

GovLLM-7B-ultra

GovLLM-7B-ultra

Model description

Usage with Inference Endpoints (Dedicated)

Training hyperparameters

Training results

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