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KeerthiSaiKasaraneni/
danish_model_secondary_finetuned_model

Name: danish_model_secondary_finetuned_model
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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:

Task Specialization: Primarily optimized for Danish translation and instruction tasks. May exhibit reduced performance outside the intended scope.
Contextual and Domain Limitations: Complex or domain-specific prompts may require additional validation of accuracy and nuance.
Ethical and Safety Considerations: Outputs may need review for correctness, fairness, and appropriateness. Users should implement safety measures before deploying in sensitive applications.
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

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}
}