Instructions to use msy127/ft_240201_01 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use msy127/ft_240201_01 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="msy127/ft_240201_01")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("msy127/ft_240201_01") model = AutoModelForCausalLM.from_pretrained("msy127/ft_240201_01") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use msy127/ft_240201_01 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "msy127/ft_240201_01" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "msy127/ft_240201_01", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/msy127/ft_240201_01
- SGLang
How to use msy127/ft_240201_01 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 "msy127/ft_240201_01" \ --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": "msy127/ft_240201_01", "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 "msy127/ft_240201_01" \ --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": "msy127/ft_240201_01", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use msy127/ft_240201_01 with Docker Model Runner:
docker model run hf.co/msy127/ft_240201_01
license: llama2 language: - ko library_name: transformers base_model: beomi/llama-2-ko-7b pipeline_tag: text-generation
msy127/ft_240201_01
Our Team
| Research & Engineering | Product Management |
|---|---|
| David Sohn | David Sohn |
Model Details
Base Model
Trained On
- OS: Ubuntu 22.04
- GPU: A100 40GB 1ea
- transformers: v4.37
Instruction format
It follows Custom format.
E.g.
text = """\
<|user|>
๊ฑด๊ฐํ ์์ต๊ด์ ๋ง๋ค๊ธฐ ์ํด์๋ ์ด๋ป๊ฒ ํ๋๊ฒ์ด ์ข์๊น์?
<|assistant|>
"""
Implementation Code
This model contains the chat_template instruction format.
You can use the code below.
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="msy127/ft_240201_01")
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("msy127/ft_240201_01")
model = AutoModelForCausalLM.from_pretrained("msy127/ft_240201_01")
Introduction to our service platform
- AI Companion service platform that talks while looking at your face.
- You can preview the future of the world's best, character.ai.
- https://livetalkingai.com
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