Instructions to use ponoma16/CodeKobzar13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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!"

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