Sentence Similarity
sentence-transformers
GGUF
Transformers
English
feature-extraction
drilling-engineering
Instructions to use GainEnergy/OGAI-Embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use GainEnergy/OGAI-Embedder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GainEnergy/OGAI-Embedder") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use GainEnergy/OGAI-Embedder with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("GainEnergy/OGAI-Embedder", dtype="auto") - llama-cpp-python
How to use GainEnergy/OGAI-Embedder with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="GainEnergy/OGAI-Embedder", filename="ogai-embedder-q5_0.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 GainEnergy/OGAI-Embedder with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf GainEnergy/OGAI-Embedder:Q5_0 # Run inference directly in the terminal: llama-cli -hf GainEnergy/OGAI-Embedder:Q5_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf GainEnergy/OGAI-Embedder:Q5_0 # Run inference directly in the terminal: llama-cli -hf GainEnergy/OGAI-Embedder:Q5_0
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 GainEnergy/OGAI-Embedder:Q5_0 # Run inference directly in the terminal: ./llama-cli -hf GainEnergy/OGAI-Embedder:Q5_0
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 GainEnergy/OGAI-Embedder:Q5_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf GainEnergy/OGAI-Embedder:Q5_0
Use Docker
docker model run hf.co/GainEnergy/OGAI-Embedder:Q5_0
- LM Studio
- Jan
- Ollama
How to use GainEnergy/OGAI-Embedder with Ollama:
ollama run hf.co/GainEnergy/OGAI-Embedder:Q5_0
- Unsloth Studio new
How to use GainEnergy/OGAI-Embedder 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 GainEnergy/OGAI-Embedder 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 GainEnergy/OGAI-Embedder to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for GainEnergy/OGAI-Embedder to start chatting
- Docker Model Runner
How to use GainEnergy/OGAI-Embedder with Docker Model Runner:
docker model run hf.co/GainEnergy/OGAI-Embedder:Q5_0
- Lemonade
How to use GainEnergy/OGAI-Embedder with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull GainEnergy/OGAI-Embedder:Q5_0
Run and chat with the model
lemonade run user.OGAI-Embedder-Q5_0
List all available models
lemonade list
Upload README.md with huggingface_hub
Browse files
README.md
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---
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base_model: sentence-transformers/all-MiniLM-L6-v2
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datasets:
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- s2orc
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- flax-sentence-embeddings/stackexchange_xml
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- ms_marco
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- gooaq
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- yahoo_answers_topics
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- code_search_net
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- search_qa
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- eli5
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- snli
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- multi_nli
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- wikihow
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- natural_questions
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- trivia_qa
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- embedding-data/sentence-compression
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- embedding-data/flickr30k-captions
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- embedding-data/altlex
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- embedding-data/simple-wiki
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- embedding-data/QQP
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- embedding-data/SPECTER
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- embedding-data/PAQ_pairs
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- embedding-data/WikiAnswers
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language: en
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library_name: sentence-transformers
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license: apache-2.0
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pipeline_tag: sentence-similarity
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tags:
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- sentence-transformers
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- feature-extraction
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- sentence-similarity
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- transformers
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- llama-cpp
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---
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# tommytracx/all-MiniLM-L6-v2-Q5_0-GGUF
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This model was converted to GGUF format from [`sentence-transformers/all-MiniLM-L6-v2`](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) using llama.cpp.
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Refer to the [original model card](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) for more details on the model.
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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```bash
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brew install llama.cpp
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```
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Invoke the llama.cpp server or the CLI.
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### CLI:
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```bash
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llama-cli --hf-repo tommytracx/all-MiniLM-L6-v2-Q5_0-GGUF --hf-file all-minilm-l6-v2-q5_0.gguf -p "The meaning to life and the universe is"
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```
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### Server:
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```bash
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llama-server --hf-repo tommytracx/all-MiniLM-L6-v2-Q5_0-GGUF --hf-file all-minilm-l6-v2-q5_0.gguf -c 2048
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```
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
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Step 1: Clone llama.cpp from GitHub.
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```
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git clone https://github.com/ggerganov/llama.cpp
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```
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Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
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```
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cd llama.cpp && LLAMA_CURL=1 make
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```
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Step 3: Run inference through the main binary.
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```
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./llama-cli --hf-repo tommytracx/all-MiniLM-L6-v2-Q5_0-GGUF --hf-file all-minilm-l6-v2-q5_0.gguf -p "The meaning to life and the universe is"
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```
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or
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```
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./llama-server --hf-repo tommytracx/all-MiniLM-L6-v2-Q5_0-GGUF --hf-file all-minilm-l6-v2-q5_0.gguf -c 2048
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```
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