tomdickson/riksbanken-mistral-lora

Riksbanken Mistral LoRA

Swedish LoRA adapters for Mistral-7B-Instruct, fine-tuned on Riksbanken (Swedish Central Bank) monetary policy reports.

Model Description

This model is a LoRA (Low-Rank Adaptation) fine-tune of mistralai/Mistral-7B-Instruct-v0.3 trained on synthetic Q&A pairs generated from Riksbanken's monetary policy reports (2022-2025).

Training Data

• Dataset: tomdickson/riksbanken-qa
• Examples: ~5,000 Swedish Q&A pairs
• Topics: Monetary policy, inflation, interest rates (reporäntan), economic forecasts

Training Configuration

• LoRA rank: 16
• LoRA alpha: 16
• Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Epochs: 1
• Learning rate: 2e-4

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-Instruct-v0.3",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

# Load LoRA adapters
model = PeftModel.from_pretrained(base_model, "tomdickson/riksbanken-mistral-lora")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")

# Generate
messages = [{"role": "user", "content": "Vad är reporäntan?"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs.to("cuda"), max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Demo

Try the model at: https://swesovereignai.web.app

Training

See the Finetuning LLMs project for training code.

License

Apache 2.0