danish_model_primary_finetuned_model

---

license: apache-2.0 license_link: https://www.apache.org/licenses/LICENSE-2.0 language:

• da base_model: unsloth/llama-3-8b-bnb-4bit tags:
• translation
• instruction-following
• danish library_name: transformers

---

Danish Model Primary Finetuned

Introduction

Danish Model Primary Finetuned is an experimental research model designed to enhance Danish language understanding and translation capabilities. This model, derived from a LLaMA-based checkpoint with LoRA adaptations, showcases promising improvements in Danish translation tasks, as well as the ability to follow instructions and produce coherent Danish responses. While this is a fine-tuned release, there remain important caveats:

1. Language Specificity: Primarily tailored for Danish translation and instruction-following, performance may vary with English inputs or non-target languages.
2. Contextual Challenges: The model may occasionally produce content that should be carefully verified for accuracy, especially in specialized domains.
3. Ethical and Safety Considerations: Additional safety layers are advised, as outputs may need review before deployment in sensitive or production environments.
4. Performance and Benchmark Limitations: Although improved in handling Danish instructions and translations, there is room for enhancement in common sense reasoning, nuanced language understanding, and more complex tasks.

Specification:

• Type: Causal Language Model
• Training Stage: Pretraining & Post-training (LoRA fine-tuning)
• Architecture: LLaMA-based with 4-bit quantization, RoPE, SwiGLU, RMSNorm, and efficient attention mechanisms
• Context Length: Up to 2,048 tokens (depending on tokenizer and architecture)
• Base Model: unsloth/llama-3-8b-bnb-4bit

For more details on LoRA and other involved techniques, refer to the associated documentation, tutorials, and the base model's README.

Requirements

This model relies on the latest versions of the Hugging Face transformers library and related dependencies. Using outdated versions may result in compatibility issues or decreased performance.

Recommended Setup:

• transformers ≥ 4.37.0
• torch (with CUDA support for GPU acceleration)
• bitsandbytes, accelerate, peft, trl, and unsloth for specialized loading and inference

Quickstart

Below is a sample snippet illustrating how to load this model and generate responses, following a prompt-based instruction format that the model was trained on.

import torch
from transformers import pipeline
from unsloth import FastLanguageModel

model_name = "KeerthiSaiKasaraneni/danish_model_primary_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 instruction
instruction = "Translate the following sentence into Danish."
user_input = "What is the capital of Denmark?"
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_primary_finetuned,
    title = {Danish Model Primary Finetuned},
    url = {https://huggingface.co/KeerthiSaiKasaraneni/danish_model_primary_finetuned_model},
    author = {[Author/Team Name]},
    month = {December},
    year = {2024}
}