Instructions to use ponoma16/CodeKobzar13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ponoma16/CodeKobzar13B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ponoma16/CodeKobzar13B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ponoma16/CodeKobzar13B") model = AutoModelForCausalLM.from_pretrained("ponoma16/CodeKobzar13B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ponoma16/CodeKobzar13B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ponoma16/CodeKobzar13B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ponoma16/CodeKobzar13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ponoma16/CodeKobzar13B
- SGLang
How to use ponoma16/CodeKobzar13B 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 "ponoma16/CodeKobzar13B" \ --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": "ponoma16/CodeKobzar13B", "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 "ponoma16/CodeKobzar13B" \ --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": "ponoma16/CodeKobzar13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ponoma16/CodeKobzar13B with Docker Model Runner:
docker model run hf.co/ponoma16/CodeKobzar13B
metadata
datasets:
- osyvokon/zno
- byebyebye/ukr-wiki-qa-v1
- byebyebye/ukr-wiki-qa-v2
language:
- uk
Introduction
CodeKobzar13B is a generative model that was trained on Ukrainian Wikipedia data and Ukrainian language rules. It has knowledge of Ukrainian history, language, literature and culture.
Model Information
This model is based on vicuna-13b-v1.5.
Model Usage
Use the following prompt template:
USER: {input} ASSISTANT:
We recommend using next configurations:
Temperature: 0.8
Top-p: 0.95
Inference
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_path="ponoma16/CodeKobzar13B"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_load_path,
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
load_in_8bit=True,
device_map='auto',
)
model.eval()
prompt = "Яке місто в Україні називають найромантичнішим?"
PROMPT_TEMPLATE = """USER: {prompt} ASSISTANT: """
input_ids = tokenizer(
prompt,
return_tensors="pt",
truncation=True,
).input_ids.cuda()
outputs = model.generate(
input_ids=input_ids,
do_sample=True,
top_p=0.95,
max_new_tokens=150,
temperature=0.5,
)
prediction = tokenizer.batch_decode(outputs.cpu().numpy(), skip_special_tokens=True)[0]
print(prediction)
Contact
If you have any inquiries, please feel free to raise an issue or reach out to us via email at: mariiaponomarenko10@gmail.com, benjamin.ye@me.com. We're here to assist you!"