Instructions to use kotlarmilos/repository-learning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kotlarmilos/repository-learning with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="kotlarmilos/repository-learning")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kotlarmilos/repository-learning", dtype="auto")

sentence-transformers

How to use kotlarmilos/repository-learning with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("kotlarmilos/repository-learning")

sentences = [
    "The weather is lovely today.",
    "It's so sunny outside!",
    "He drove to the stadium."
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kotlarmilos/repository-learning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kotlarmilos/repository-learning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kotlarmilos/repository-learning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kotlarmilos/repository-learning

SGLang

How to use kotlarmilos/repository-learning 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 "kotlarmilos/repository-learning" \
    --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": "kotlarmilos/repository-learning",
		"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 "kotlarmilos/repository-learning" \
        --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": "kotlarmilos/repository-learning",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kotlarmilos/repository-learning with Docker Model Runner:
```
docker model run hf.co/kotlarmilos/repository-learning
```

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