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@@ -40,3 +40,4 @@ gguf/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 gguf_new/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 lora/tokenizer.json filter=lfs diff=lfs merge=lfs -text
 merged/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+korkortlarare-f16.gguf filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,59 +1,76 @@
 ---
-base_model: unsloth/Qwen2.5-7B-Instruct
+license: apache-2.0
+language:
+  - sv
 library_name: transformers
-model_name: korkortlarare
+base_model: Qwen/Qwen2.5-7B-Instruct
 tags:
-- generated_from_trainer
-- trl
-- unsloth
-- sft
-licence: license
+  - gguf
+  - swedish
+  - driving-license
+  - körkort
+  - fine-tuned
+  - qwen2
+pipeline_tag: text-generation
 ---
 
-# Model Card for korkortlarare
+# Korkortlarare - Swedish Driving License Theory Assistant
 
-This model is a fine-tuned version of [unsloth/Qwen2.5-7B-Instruct](https://huggingface.co/unsloth/Qwen2.5-7B-Instruct).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+A fine-tuned Qwen2.5-7B-Instruct model specialized for Swedish driving license theory (körkortsteori).
 
-## Quick start
+## Model Details
 
-```python
-from transformers import pipeline
+- **Base Model:** [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
+- **Fine-tuning Method:** LoRA with Unsloth
+- **Training Data:** 4,080 Swedish driving license Q&A pairs
+- **Format:** GGUF F16 (full precision)
+- **Size:** 15.2 GB
+- **Language:** Swedish (sv)
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
+## Intended Use
+
+This model is designed to help users study for the Swedish driving license theory test (körkortsteori). It can answer questions about:
+
+- Traffic rules (Trafikregler)
+- Road signs (Vägmärken)
+- Safety (Säkerhet)
+- Vehicle and environment (Fordon och Miljö)
+- Parking and special situations
+- Laws and regulations
+- Practical driving
+
+## Usage with Ollama
+
+```bash
+ollama run SlipsKnuten/korkortlarare
 ```
 
-## Training procedure
+Or download and create locally:
 
- 
+```bash
+ollama create korkortlarare -f Modelfile
+```
 
+## Usage with llama.cpp
 
-This model was trained with SFT.
+```bash
+./main -m korkortlarare-f16.gguf -p "Vad innebär högerregeln?"
+```
 
-### Framework versions
+## Training
 
-- TRL: 0.24.0
-- Transformers: 4.57.3
-- Pytorch: 2.9.1+cu128
-- Datasets: 4.3.0
-- Tokenizers: 0.22.1
+The model was fine-tuned using:
+- **Framework:** Unsloth + TRL
+- **Method:** SFT (Supervised Fine-Tuning) with LoRA
+- **Epochs:** 5
+- **Dataset:** Custom Swedish driving license Q&A dataset
 
-## Citations
+## Limitations
 
+- The model is specifically trained for Swedish driving license theory and may not perform well on other topics
+- Always verify important information with official sources (Trafikverket)
+- This is an educational tool and should not replace official study materials
 
+## License
 
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
+Apache 2.0

korkortlarare-f16.gguf

@@ -0,0 +1,3 @@
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+size 15238457376