Translation
Transformers
PyTorch
English
Korean
llama
text-generation
llama-3-ko
text-generation-inference
Instructions to use 4yo1/llama3-eng-ko-8b-sl5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 4yo1/llama3-eng-ko-8b-sl5 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="4yo1/llama3-eng-ko-8b-sl5")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("4yo1/llama3-eng-ko-8b-sl5") model = AutoModelForCausalLM.from_pretrained("4yo1/llama3-eng-ko-8b-sl5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
Model Details
Model Card: LLaMA3-ENG-KO-8B-SL5 with Fine-Tuning Model Overview Model Name: LLaMA3-ENG-KO-8B-SL5
Model Type: Transformer-based Language Model
Model Size: 8 billion parameters
by: 4yo1
Languages: English and Korean
Model Description
LLaMA3-ENG-KO-8B-SL5 is a language model pre-trained on a diverse corpus of English and Korean texts. This fine-tuning approach allows the model to adapt to specific tasks or datasets with a minimal number of additional parameters, making it efficient and effective for specialized applications.
how to use - sample code
from transformers import AutoConfig, AutoModel, AutoTokenizer
config = AutoConfig.from_pretrained("4yo1/llama3-eng-ko-80-sl5")
model = AutoModel.from_pretrained("4yo1/llama3-eng-ko-8b-sl5")
tokenizer = AutoTokenizer.from_pretrained("4yo1/llama3-eng-ko-8b-sl5")
datasets:
- 4yo1/llama3_test1
license: mit
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