Instructions to use second-state/All-MiniLM-L6-v2-Embedding-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use second-state/All-MiniLM-L6-v2-Embedding-GGUF with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("second-state/All-MiniLM-L6-v2-Embedding-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]

Transformers

How to use second-state/All-MiniLM-L6-v2-Embedding-GGUF with Transformers:

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

pipe = pipeline("feature-extraction", model="second-state/All-MiniLM-L6-v2-Embedding-GGUF")

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("second-state/All-MiniLM-L6-v2-Embedding-GGUF")
model = AutoModel.from_pretrained("second-state/All-MiniLM-L6-v2-Embedding-GGUF")

llama-cpp-python

How to use second-state/All-MiniLM-L6-v2-Embedding-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="second-state/All-MiniLM-L6-v2-Embedding-GGUF",
	filename="all-MiniLM-L6-v2-Q2_K.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 second-state/All-MiniLM-L6-v2-Embedding-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf second-state/All-MiniLM-L6-v2-Embedding-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 second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/All-MiniLM-L6-v2-Embedding-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 second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M

Use Docker

docker model run hf.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use second-state/All-MiniLM-L6-v2-Embedding-GGUF with Ollama:
```
ollama run hf.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M
```

Unsloth Studio new

How to use second-state/All-MiniLM-L6-v2-Embedding-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 second-state/All-MiniLM-L6-v2-Embedding-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 second-state/All-MiniLM-L6-v2-Embedding-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for second-state/All-MiniLM-L6-v2-Embedding-GGUF to start chatting

Docker Model Runner
How to use second-state/All-MiniLM-L6-v2-Embedding-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M
```

Lemonade

How to use second-state/All-MiniLM-L6-v2-Embedding-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/All-MiniLM-L6-v2-Embedding-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.All-MiniLM-L6-v2-Embedding-GGUF-Q4_K_M

List all available models

lemonade list

All-MiniLM-L6-v2-GGUF

Original Model

sentence-transformers/all-MiniLM-L6-v2

Run with LlamaEdge

LlamaEdge version: v0.8.2 and above
Context size: 384
Vector size: 256

Run as LlamaEdge service

wasmedge --dir .:. --nn-preload default:GGML:AUTO:all-MiniLM-L6-v2-ggml-model-f16.gguf \
  llama-api-server.wasm \
  --prompt-template llama-2-chat \
  --ctx-size 256 \
  --model-name all-MiniLM-L6-v2

Quantized GGUF Models

Name	Quant method	Bits	Size	Use case
all-MiniLM-L6-v2-Q2_K.gguf	Q2_K	2	19.2 MB	smallest, significant quality loss - not recommended for most purposes
all-MiniLM-L6-v2-Q3_K_L.gguf	Q3_K_L	3	20.5 MB	small, substantial quality loss
all-MiniLM-L6-v2-Q3_K_M.gguf	Q3_K_M	3	19.9 MB	very small, high quality loss
all-MiniLM-L6-v2-Q3_K_S.gguf	Q3_K_S	3	19.2 MB	very small, high quality loss
all-MiniLM-L6-v2-Q4_0.gguf	Q4_0	4	19.7 MB	legacy; small, very high quality loss - prefer using Q3_K_M
all-MiniLM-L6-v2-Q4_K_M.gguf	Q4_K_M	4	21 MB	medium, balanced quality - recommended
all-MiniLM-L6-v2-Q4_K_S.gguf	Q4_K_S	4	20.7 MB	small, greater quality loss
all-MiniLM-L6-v2-Q5_0.gguf	Q5_0	5	21 MB	legacy; medium, balanced quality - prefer using Q4_K_M
all-MiniLM-L6-v2-Q5_K_M.gguf	Q5_K_M	5	21.7 MB	large, very low quality loss - recommended
all-MiniLM-L6-v2-Q5_K_S.gguf	Q5_K_S	5	21.5 MB	large, low quality loss - recommended
all-MiniLM-L6-v2-Q6_K.gguf	Q6_K	6	24.2 MB	very large, extremely low quality loss
all-MiniLM-L6-v2-Q8_0.gguf	Q8_0	8	25 MB	very large, extremely low quality loss - not recommended
all-MiniLM-L6-v2-ggml-model-f16.gguf	Q8_0	8	45.9 MB	very large, extremely low quality loss - not recommended

Quantized with llama.cpp b2334

Downloads last month: 16,197

GGUF

Model size

22.6M params

Architecture

bert

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for second-state/All-MiniLM-L6-v2-Embedding-GGUF

Base model

sentence-transformers/all-MiniLM-L6-v2

Quantized

(78)

this model

Finetunes

1 model

second-state
/

All-MiniLM-L6-v2-Embedding-GGUF

All-MiniLM-L6-v2-GGUF

Original Model

Run with LlamaEdge

Quantized GGUF Models

Model tree for second-state/All-MiniLM-L6-v2-Embedding-GGUF

Space using second-state/All-MiniLM-L6-v2-Embedding-GGUF 1