Instructions to use mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF with Transformers:

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

pipe = pipeline("feature-extraction", model="mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF", dtype="auto")

sentence-transformers

How to use mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-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 mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF",
	filename="KaLM-embedding-multilingual-mini-instruct-v1.5.IQ4_XS.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 mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF 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 mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF: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 mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF: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 mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF with Ollama:
```
ollama run hf.co/mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF:Q4_K_M
```

Unsloth Studio

How to use mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-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 mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-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 mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF-Q4_K_M

List all available models

lemonade list

KaLM-embedding-multilingual-mini-instruct-v1.5-GGUF / KaLM-embedding-multilingual-mini-instruct-v1.5.Q5_K_S.gguf

mradermacher

uploaded from nico1

722ec60 verified about 1 year ago

Download

History Blame GGUF Editor new Delete

413 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: fa98ca8b49dbbe778b7ce3bd60f3f78f8884924b418dae75670c5dd1a5e0657f
Size of remote file:: 413 MB
SHA256:: 62c76b1b7acac9df8ca58080e561e83ddf94642343eb9b0242734a30e22a08d9

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.