peft safetensors gguf mistral nl base_model:unsloth/Mistral-Small-24B-Instruct-2501-unsloth-bnb-4bit base_model:adapter:unsloth/Mistral-Small-24B-Instruct-2501-unsloth-bnb-4bit license:apache-2.0 endpoints_compatible region:us conversational
---
base_model: unsloth/Mistral-Small-24B-Instruct-2501-unsloth-bnb-4bit
language:
- nl
library_name: peft
license: apache-2.0
---

# Model Card for Mistral-Small-24B-Instruct-2501-Dutch-GGUF

This is a fine-tuned version of Mistral-Small-24B-Instruct that has been specifically optimized for Dutch language understanding and generation. This model was developed by Aisk to improve Dutch language capabilities while maintaining the strong instruction-following abilities of the base model.


## Model Details

### Model Description

Mistral-Small-24B-Instruct-unsloth-bnb-4bit-2501-Dutch is a fine-tuned version of Mistral-Small-24B-Instruct that has been specifically optimized for Dutch language understanding and generation. This model was developed by Aisk to improve Dutch language capabilities while maintaining the strong instruction-following abilities of the base model.

The model has been quantized to 4-bit precision using BitsAndBytes (bnb) and optimized with Unsloth for efficient inference, making it more accessible for deployment on consumer hardware while preserving most of the language capabilities.


- **Developed by:** [Aisk](https://getaisk.com) (Dutch website: [Aisk](https://aisk.nl))
- **Model type:** Q4, Q8 and F16 GGUF and safetensor model finetuned using PEFT.
- **Language(s) (NLP):** NL / Dutch, EN / English
- **Finetuned from model [optional]:** Mistral-Small-24B-Instruct-2501

### Model Sources [optional]

2587 Dutch books have been used to train. Roughly `350 million` tokens were in the dataset.

We are working on expanding this amount and improve this model even further.

## Uses

### Direct Use

This model is particularly well-suited for:

- Dutch language conversation and chat applications
- Dutch content generation and summarization
- Translation assistance to and from Dutch
- Question answering in Dutch
- Dutch language understanding tasks


### Recommendations

## How to Get Started with the Model

Use the code below to get started with the model.

```
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "GetAisk/Mistral-Small-24B-Instruct-unsloth-bnb-4bit-2501-Dutch"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    load_in_4bit=True
)

# Example Dutch prompt
prompt = """<s>[INST] Vertel me iets over de Nederlandse cultuur. [/INST]"""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=500)
print(tokenizer.decode(output[0], skip_special_tokens=False))
```

## Training Details

### Training Data

2587 Dutch ebooks have been used. Roughly 350 million tokens.

### Training Procedure

Fine tuned model using LoRa based on Unsloth.

- PEFT 0.14.0