Feature Extraction
Transformers
GGUF
sentence-transformers
English
sentence-similarity
dense
Generated from Trainer
dataset_size:291522
loss:MultipleNegativesSymmetricRankingLoss
Instructions to use mradermacher/MiniLM-V6Data-SemanticEngine-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/MiniLM-V6Data-SemanticEngine-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mradermacher/MiniLM-V6Data-SemanticEngine-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/MiniLM-V6Data-SemanticEngine-GGUF", device_map="auto") - sentence-transformers
How to use mradermacher/MiniLM-V6Data-SemanticEngine-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mradermacher/MiniLM-V6Data-SemanticEngine-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] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/MiniLM-V6Data-SemanticEngine-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/MiniLM-V6Data-SemanticEngine-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/MiniLM-V6Data-SemanticEngine-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/MiniLM-V6Data-SemanticEngine-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/MiniLM-V6Data-SemanticEngine-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/MiniLM-V6Data-SemanticEngine-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/MiniLM-V6Data-SemanticEngine-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/MiniLM-V6Data-SemanticEngine-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/MiniLM-V6Data-SemanticEngine-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/MiniLM-V6Data-SemanticEngine-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/MiniLM-V6Data-SemanticEngine-GGUF with Ollama:
ollama run hf.co/mradermacher/MiniLM-V6Data-SemanticEngine-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use mradermacher/MiniLM-V6Data-SemanticEngine-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/MiniLM-V6Data-SemanticEngine-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/MiniLM-V6Data-SemanticEngine-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/MiniLM-V6Data-SemanticEngine-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.MiniLM-V6Data-SemanticEngine-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
uploaded from leia
Browse files- .gitattributes +12 -0
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- MiniLM-V6Data-SemanticEngine.Q8_0.gguf +3 -0
- MiniLM-V6Data-SemanticEngine.f16.gguf +3 -0
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