Instructions to use junga/hjys_LLM_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junga/hjys_LLM_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="junga/hjys_LLM_final")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("junga/hjys_LLM_final") model = AutoModelForCausalLM.from_pretrained("junga/hjys_LLM_final") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use junga/hjys_LLM_final with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "junga/hjys_LLM_final" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "junga/hjys_LLM_final", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/junga/hjys_LLM_final
- SGLang
How to use junga/hjys_LLM_final with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "junga/hjys_LLM_final" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "junga/hjys_LLM_final", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "junga/hjys_LLM_final" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "junga/hjys_LLM_final", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use junga/hjys_LLM_final with Docker Model Runner:
docker model run hf.co/junga/hjys_LLM_final
Model Card: hjys_LLM_final (42dot LLM-SFT-1.3B Fine-Tuned Version)
Model Overview
The 42dot LLM-SFT-1.3B is a fine-tuned version of the large language model developed by 42dot, specifically undergoing Supervised Fine-Tuning (SFT) to enhance its ability to follow natural language instructions. This model aims to improve scores on the ko-CommonGen V2 task, for which it was fine-tuned using the beomi/KoAlpaca-v1.1a dataset.
Dataset
The beomi/KoAlpaca-v1.1a dataset used for fine-tuning offers a rich resource for Korean natural language processing, contributing to the advancement of the model's language understanding and generation capabilities.
Goal
The primary goal of this model is to improve scores on the ko-CommonGen V2 task, which involves generating meaningful sentences using given words, assessing the model's creativity and language comprehension. This model is equipped to effectively use specific keywords to generate meaningful sentences.
Fine-Tuning Details
- Parameters: 1.3B
- Layers: 24
- Attention Heads: 32
- Hidden Size: 2,048
- FFN Size: 5,632
- Maximum Length: 4,096 tokens
- Training Time: 5 GPU hours on NVIDIA A100 (Google Colab Pro+)
Limitations and Ethical Considerations
Like other LLMs, the 42dot LLM-SFT-1.3B may produce hallucinated or biased content. Users should be aware of these limitations and take appropriate actions.
Disclaimer
Contents generated by this model do not necessarily reflect the views of 42dot Inc. All responsibility lies with the end-user, and 42dot assumes no liability.
License
This model is available for non-commercial use only, under the Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0) license.
- Downloads last month
- 7