Instructions to use colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF")

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]

llama-cpp-python

How to use colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF",
	filename="KaLM-embedding-multilingual-mini-instruct-v2-F16.gguf",
)

llm.create_chat_completion(
	messages = "\"Today is a sunny day and I will get some ice cream.\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF:F16

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 colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF:F16

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 colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF:F16

Use Docker

docker model run hf.co/colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF:F16

LM Studio
Jan
Ollama
How to use colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF with Ollama:
```
ollama run hf.co/colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF:F16
```

Unsloth Studio

How to use colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF to start chatting

Docker Model Runner
How to use colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF:F16
```

Lemonade

How to use colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF:F16

Run and chat with the model

lemonade run user.KaLM-embedding-multilingual-mini-instruct-v2-GGUF-F16

List all available models

lemonade list

KaLM-embedding-multilingual-mini-instruct-v2

Model creator: HIT-TMG
Original model: HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v2
GGUF quantization: provided by colintoal using llama.cpp

Special thanks

🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.

Use with Ollama

ollama run "hf.co/colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF:F16"

Use with LM Studio

lms load "colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF"

Use with llama.cpp CLI

llama-cli --hf "colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF:F16" -p "The meaning to life and the universe is"

Use with llama.cpp Server:

llama-server --hf "colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF:F16" -c 4096

Downloads last month: 14

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

16-bit

Model tree for colintoal/KaLM-embedding-multilingual-mini-instruct-v2-GGUF

Base model

Qwen/Qwen2-0.5B

Finetuned

HIT-TMG/KaLM-embedding-multilingual-mini-instruct-v2

Quantized

(1)

this model

colintoal
/

KaLM-embedding-multilingual-mini-instruct-v2-GGUF