safetensors mistral license:mit region:us
---
license: mit
---
---
license: apache-2.0
license_link: https://www.apache.org/licenses/LICENSE-2.0
language:
- da
base_model: unsloth/zephyr-sft-bnb-4bit
tags:
- translation
- instruction-following
- danish
library_name: transformers
---

# Danish Model Secondary Finetuned

## Introduction

**Danish Model Secondary Finetuned** is an experimental research model focusing on Danish language understanding, building upon the Zephyr architecture in a 4-bit quantized format. This model represents a secondary stage of adaptation, fine-tuned to further improve Danish translation quality, instruction-following behavior, and text generation capabilities. While showcasing improved translation fidelity and responsiveness, it still comes with important caveats:

1. **Task Specialization**: Primarily optimized for Danish translation and instruction tasks. May exhibit reduced performance outside the intended scope.
2. **Contextual and Domain Limitations**: Complex or domain-specific prompts may require additional validation of accuracy and nuance.
3. **Ethical and Safety Considerations**: Outputs may need review for correctness, fairness, and appropriateness. Users should implement safety measures before deploying in sensitive applications.
4. **Performance Boundaries**: While improved in certain dimensions, there is potential for growth in reasoning depth, language adaptability, and handling of intricate linguistic nuances.

**Specification**:
- Type: Causal Language Model
- Training Stage: Pretraining & Post-Training (LoRA-based fine-tuning)
- Architecture: Zephyr-based, 4-bit quantization, RoPE, SwiGLU, RMSNorm, and attention optimizations
- Context Length: Up to 2,048 tokens (may depend on tokenizer and model configuration)
- Base Model: [unsloth/zephyr-sft-bnb-4bit](https://huggingface.co/unsloth/zephyr-sft-bnb-4bit)

Refer to the base model documentation and LoRA technique guides for more insights into the underlying approaches and capabilities.

## Requirements

This model is built using Hugging Face `transformers` and related libraries. Using the latest versions will ensure compatibility and optimal performance.

**Recommended Setup**:
- `transformers` ≥ 4.37.0
- `torch` (with CUDA if GPU acceleration is desired)
- `bitsandbytes`, `accelerate`, `peft`, `trl`, and `unsloth` for efficient loading and inference

## Quickstart

Below is an example to help you start generating outputs with the Danish Model Secondary Finetuned. Adapt this code snippet for your own tasks and prompts.

```python
import torch
from transformers import pipeline
from unsloth import FastLanguageModel

model_name = "KeerthiSaiKasaraneni/danish_model_secondary_finetuned_model"

# Load model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=model_name,
    load_in_4bit=True,
    dtype=torch.float16
)
FastLanguageModel.for_inference(model)

# Create a text generation pipeline
generation_pipeline = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    device_map="auto"
)

# Example usage
instruction = "Translate the following sentence into Danish."
user_input = "The quick brown fox jumps over the lazy dog."
prompt = f"Instruction: {instruction} Input: {user_input} Output:"

generated = generation_pipeline(prompt, max_length=128, num_return_sequences=1)
response = generated[0]['generated_text']
print(response)
```




## Citation

If you find our work helpful, feel free to give us a cite.

```
@misc{danish_model_secondary_finetuned,
    title = {Danish Model Secondary Finetuned},
    url = {https://huggingface.co/KeerthiSaiKasaraneni/danish_model_secondary_finetuned_model},
    author = {[Author/Team Name]},
    month = {December},
    year = {2024}
}

```