Instructions to use asg017/sqlite-lembed-model-examples with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use asg017/sqlite-lembed-model-examples with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="asg017/sqlite-lembed-model-examples",
	filename="all-MiniLM-L6-v2/all-MiniLM-L6-v2.e4ce9877.bf16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use asg017/sqlite-lembed-model-examples with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf asg017/sqlite-lembed-model-examples:BF16
# Run inference directly in the terminal:
llama-cli -hf asg017/sqlite-lembed-model-examples:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf asg017/sqlite-lembed-model-examples:BF16
# Run inference directly in the terminal:
llama-cli -hf asg017/sqlite-lembed-model-examples:BF16

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 asg017/sqlite-lembed-model-examples:BF16
# Run inference directly in the terminal:
./llama-cli -hf asg017/sqlite-lembed-model-examples:BF16

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 asg017/sqlite-lembed-model-examples:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf asg017/sqlite-lembed-model-examples:BF16

Use Docker

docker model run hf.co/asg017/sqlite-lembed-model-examples:BF16

LM Studio
Jan
Ollama
How to use asg017/sqlite-lembed-model-examples with Ollama:
```
ollama run hf.co/asg017/sqlite-lembed-model-examples:BF16
```

Unsloth Studio new

How to use asg017/sqlite-lembed-model-examples 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 asg017/sqlite-lembed-model-examples 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 asg017/sqlite-lembed-model-examples to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for asg017/sqlite-lembed-model-examples to start chatting

Docker Model Runner
How to use asg017/sqlite-lembed-model-examples with Docker Model Runner:
```
docker model run hf.co/asg017/sqlite-lembed-model-examples:BF16
```

Lemonade

How to use asg017/sqlite-lembed-model-examples with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull asg017/sqlite-lembed-model-examples:BF16

Run and chat with the model

lemonade run user.sqlite-lembed-model-examples-BF16

List all available models

lemonade list

sentence-transformers==2.7.0

$ cat load.py 
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
$ TRANSFORMERS_CACHE=$PWD/cache python load.py

./llama.cpp/convert-hf-to-gguf.py \
  cache/models--sentence-transformers--all-MiniLM-L6-v2/snapshots/e4ce9877abf3edfe10b0d82785e83bdcb973e22e/ \
  --outfile all-MiniLM-L6-v2.e4ce9877.{ftype}.gguf \
  --outtype=f32

Downloads last month: 113

GGUF

Model size

22.6M params

Architecture

bert

Hardware compatibility

8-bit

16-bit

32-bit

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