Instructions to use ai-babai/giga-embeddings-0826-480m-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ai-babai/giga-embeddings-0826-480m-gguf with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("ai-babai/giga-embeddings-0826-480m-gguf")

sentences = [
    "Это счастливый человек",
    "Это счастливая собака",
    "Это очень счастливый человек",
    "Сегодня солнечный день"
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ai-babai/giga-embeddings-0826-480m-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 ai-babai/giga-embeddings-0826-480m-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ai-babai/giga-embeddings-0826-480m-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 ai-babai/giga-embeddings-0826-480m-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ai-babai/giga-embeddings-0826-480m-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 ai-babai/giga-embeddings-0826-480m-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ai-babai/giga-embeddings-0826-480m-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 ai-babai/giga-embeddings-0826-480m-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ai-babai/giga-embeddings-0826-480m-gguf:Q4_K_M

Use Docker

docker model run hf.co/ai-babai/giga-embeddings-0826-480m-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use ai-babai/giga-embeddings-0826-480m-gguf with Ollama:
```
ollama run hf.co/ai-babai/giga-embeddings-0826-480m-gguf:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use ai-babai/giga-embeddings-0826-480m-gguf with Docker Model Runner:
```
docker model run hf.co/ai-babai/giga-embeddings-0826-480m-gguf:Q4_K_M
```

Lemonade

How to use ai-babai/giga-embeddings-0826-480m-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ai-babai/giga-embeddings-0826-480m-gguf:Q4_K_M

Run and chat with the model

lemonade run user.giga-embeddings-0826-480m-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

LM Studio не видит Giga Embeddings как embedding модель.

by TreeLoys - opened 5 days ago

Discussion

TreeLoys

5 days ago

Как должно быть (на примере другого эмбеддинга)

Type: embeddings
{ "id": "text-embedding-nomic-embed-text-v1.5", "object": "model", "type": "embeddings", "publisher": "nomic-ai", "arch": "nomic-bert", "compatibility_type": "gguf", "quantization": "Q4_K_M", "state": "not-loaded", "max_context_length": 2048 }

А отдает

Type: llm
{ "id": "giga-ai-babai", "object": "model", "type": "llm", "publisher": "embedded", "arch": "qwen3", "compatibility_type": "gguf", "quantization": "Q8_0", "state": "loaded", "max_context_length": 8192, "loaded_context_length": 8192 }

Так же в интерфейсе она определяется как LLM. Пофиксите плиз.

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