Instructions to use argyrotsipi/parliabench-unsloth-mistral-7b-v0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use argyrotsipi/parliabench-unsloth-mistral-7b-v0.3 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/mistral-7b-v0.3-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "argyrotsipi/parliabench-unsloth-mistral-7b-v0.3")

Transformers

How to use argyrotsipi/parliabench-unsloth-mistral-7b-v0.3 with Transformers:

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

pipe = pipeline("text-generation", model="argyrotsipi/parliabench-unsloth-mistral-7b-v0.3")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("argyrotsipi/parliabench-unsloth-mistral-7b-v0.3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use argyrotsipi/parliabench-unsloth-mistral-7b-v0.3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "argyrotsipi/parliabench-unsloth-mistral-7b-v0.3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "argyrotsipi/parliabench-unsloth-mistral-7b-v0.3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/argyrotsipi/parliabench-unsloth-mistral-7b-v0.3

SGLang

How to use argyrotsipi/parliabench-unsloth-mistral-7b-v0.3 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 "argyrotsipi/parliabench-unsloth-mistral-7b-v0.3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "argyrotsipi/parliabench-unsloth-mistral-7b-v0.3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "argyrotsipi/parliabench-unsloth-mistral-7b-v0.3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "argyrotsipi/parliabench-unsloth-mistral-7b-v0.3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use argyrotsipi/parliabench-unsloth-mistral-7b-v0.3 with Docker Model Runner:
```
docker model run hf.co/argyrotsipi/parliabench-unsloth-mistral-7b-v0.3
```

Model Name: <ParliaBench - unsloth/mistral-7b-v0.3-bnb-4bit>

1. Model Summary

This is a fine-tuned model developed for the research paper "ParliaBench: An Evaluation and Benchmarking Framework for LLM-Generated Parliamentary Speech".

The model is trained to:

Generate UK-style parliamentary debates
Produce party-conditioned political arguments
Model parliamentary dialogue structures
Perform topic-aware generation using 21 EuroVoc topic categories

2. Base Model

Base model: "mistralai/Mistral-7B-v0.3"

This repository contains only LoRA adapter weights.
The base model must be loaded separately.

3. Training Data

The model was trained using:

ParlaMint-GB from Clarin
Preprocessed into:
- Structured debates
- Speaker metadata
- Section headers
- 21-class topic labels

Dataset links: - https://huggingface.co/datasets/argyrotsipi/train-dataset - https://huggingface.co/datasets/argyrotsipi/generated-dataset

4. Training Procedure

Hyperparameters

Batch Size 64
Learning Rate 2e-4
Max Steps 11194 (2 epochs)
Warmup Steps 336 (10% of max steps for stability)
Optimizer adamw
Weight Decay 0.01
Max Sequence Length 1024
Scheduler linear

Hardware

GPU A100 AWS resources were provided by the National Infrastructures for Research and Technology GRNET and funded by the EU Recovery and Resiliency Facility.

LoRA Rank (r) 16
LoRA Alpha 16
Target Modules 7 layers
LoRA Dropout 0
Bias Configuration none
Random State 3407

5. Intended Uses

This model is designed for:

Political debate simulation
Parliamentary dialogue generation
Academic NLP research
Social behavior modeling

6. Limitations

May reproduce political biases present in the corpus
Not suitable for real political advice or predictions
Model outputs are synthetic and not factual

7. Ethical Considerations

Avoid using for political persuasion
Use strictly in academic and research contexts
Outputs may reflect UK political biases from the dataset

8. Citation

@misc{ParliaBench2025, title={ParliaBench: An Evaluation and Benchmarking Framework for LLM-Generated Parliamentary Speech}, author={Marios Koniaris and Argyro Tsipi and Panayiotis Tsanakas}, year={2025}, eprint={2511.08247}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2511.08247}, }