tomdickson
/

riksbanken-mistral-lora

@@ -1,209 +1,76 @@
 ---
-base_model: mistralai/Mistral-7B-Instruct-v0.3
 library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3
-- lora
-- sft
-- transformers
-- trl
 ---
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.18.0

 ---
+language:
+  - sv
+license: apache-2.0
 library_name: peft
+base_model: mistralai/Mistral-7B-Instruct-v0.3
 tags:
+  - swedish
+  - riksbanken
+  - monetary-policy
+  - finance
+  - lora
+  - mistral
+  - instruction-tuning
+datasets:
+  - tomdickson/riksbanken-qa
 ---
+# 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](https://huggingface.co/datasets/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
+```python
+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](https://github.com/t0mdicks0n/finetuning_llms) project for training code.
+## License
+Apache 2.0