Instructions to use QLNI/embeddings-are-overpriced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use QLNI/embeddings-are-overpriced with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QLNI/embeddings-are-overpriced:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QLNI/embeddings-are-overpriced:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QLNI/embeddings-are-overpriced:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QLNI/embeddings-are-overpriced:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QLNI/embeddings-are-overpriced:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QLNI/embeddings-are-overpriced:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QLNI/embeddings-are-overpriced:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QLNI/embeddings-are-overpriced:Q4_K_M

Use Docker

docker model run hf.co/QLNI/embeddings-are-overpriced:Q4_K_M

LM Studio
Jan

vLLM

How to use QLNI/embeddings-are-overpriced with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/QLNI/embeddings-are-overpriced:Q4_K_M

Ollama
How to use QLNI/embeddings-are-overpriced with Ollama:
```
ollama run hf.co/QLNI/embeddings-are-overpriced:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use QLNI/embeddings-are-overpriced with Docker Model Runner:
```
docker model run hf.co/QLNI/embeddings-are-overpriced:Q4_K_M
```

Lemonade

How to use QLNI/embeddings-are-overpriced with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QLNI/embeddings-are-overpriced:Q4_K_M

Run and chat with the model

lemonade run user.embeddings-are-overpriced-Q4_K_M

List all available models

lemonade list

Atomic Chat

embeddings-are-overpriced / code /upload_data.py

QLNI

Embeddings Are Overpriced? release: code, data, checkpoints and evaluation

f31f59b 5 days ago

Raw History Blame Contribute Delete

605 Bytes

	"""Upload the tokenised data folder to the private HF dataset QLNI/embeddings-are-overpriced-data.

	python upload_data.py --data data
	"""
	import argparse
	from huggingface_hub import HfApi

	ap = argparse.ArgumentParser()
	ap.add_argument("--data", default="data")
	a = ap.parse_args()
	api = HfApi()
	api.create_repo("QLNI/embeddings-are-overpriced-data", repo_type="dataset", private=True, exist_ok=True)
	api.upload_large_folder(repo_id="QLNI/embeddings-are-overpriced-data", repo_type="dataset", folder_path=a.data,
	allow_patterns=["*.npy"])
	print("uploaded", a.data, flush=True)