Instructions to use mradermacher/MiniLM-v27-SemanticEngine-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/MiniLM-v27-SemanticEngine-GGUF with Transformers:

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

pipe = pipeline("feature-extraction", model="mradermacher/MiniLM-v27-SemanticEngine-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/MiniLM-v27-SemanticEngine-GGUF", dtype="auto")

sentence-transformers

How to use mradermacher/MiniLM-v27-SemanticEngine-GGUF with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("mradermacher/MiniLM-v27-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]

llama-cpp-python

How to use mradermacher/MiniLM-v27-SemanticEngine-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mradermacher/MiniLM-v27-SemanticEngine-GGUF",
	filename="MiniLM-v27-SemanticEngine.IQ4_XS.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/MiniLM-v27-SemanticEngine-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mradermacher/MiniLM-v27-SemanticEngine-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/MiniLM-v27-SemanticEngine-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 mradermacher/MiniLM-v27-SemanticEngine-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/MiniLM-v27-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-v27-SemanticEngine-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/MiniLM-v27-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-v27-SemanticEngine-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/MiniLM-v27-SemanticEngine-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/MiniLM-v27-SemanticEngine-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/MiniLM-v27-SemanticEngine-GGUF with Ollama:
```
ollama run hf.co/mradermacher/MiniLM-v27-SemanticEngine-GGUF:Q4_K_M
```

Unsloth Studio

How to use mradermacher/MiniLM-v27-SemanticEngine-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 mradermacher/MiniLM-v27-SemanticEngine-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 mradermacher/MiniLM-v27-SemanticEngine-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/MiniLM-v27-SemanticEngine-GGUF to start chatting

Docker Model Runner
How to use mradermacher/MiniLM-v27-SemanticEngine-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/MiniLM-v27-SemanticEngine-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/MiniLM-v27-SemanticEngine-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/MiniLM-v27-SemanticEngine-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniLM-v27-SemanticEngine-GGUF-Q4_K_M

List all available models

lemonade list

mradermacher commited on Dec 1, 2025

Commit

cd668d5

verified ·

1 Parent(s): 4b2f18d

auto-patch README.md

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@@ -1,3 +1,22 @@
 <!-- ### quantize_version: 2 -->
 <!-- ### output_tensor_quantised: 1 -->
 <!-- ### convert_type: hf -->
@@ -7,3 +26,54 @@
 <!-- ### quants_skip:  -->
 <!-- ### skip_mmproj:  -->
 static quants of https://huggingface.co/LamaDiab/MiniLM-v27-SemanticEngine

+---
+base_model: LamaDiab/MiniLM-v27-SemanticEngine
+language:
+- en
+library_name: transformers
+mradermacher:
+  readme_rev: 1
+quantized_by: mradermacher
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:790993
+- loss:MultipleNegativesSymmetricRankingLoss
+---
+## About
 <!-- ### quantize_version: 2 -->
 <!-- ### output_tensor_quantised: 1 -->
 <!-- ### convert_type: hf -->
 <!-- ### quants_skip:  -->
 <!-- ### skip_mmproj:  -->
 static quants of https://huggingface.co/LamaDiab/MiniLM-v27-SemanticEngine
+<!-- provided-files -->
+***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#MiniLM-v27-SemanticEngine-GGUF).***
+weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
+## Usage
+If you are unsure how to use GGUF files, refer to one of [TheBloke's
+READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
+more details, including on how to concatenate multi-part files.
+## Provided Quants
+(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
+| Link | Type | Size/GB | Notes |
+|:-----|:-----|--------:|:------|
+| [GGUF](https://huggingface.co/mradermacher/MiniLM-v27-SemanticEngine-GGUF/resolve/main/MiniLM-v27-SemanticEngine.Q2_K.gguf) | Q2_K | 0.1 |  |
+| [GGUF](https://huggingface.co/mradermacher/MiniLM-v27-SemanticEngine-GGUF/resolve/main/MiniLM-v27-SemanticEngine.Q3_K_S.gguf) | Q3_K_S | 0.1 |  |
+| [GGUF](https://huggingface.co/mradermacher/MiniLM-v27-SemanticEngine-GGUF/resolve/main/MiniLM-v27-SemanticEngine.IQ4_XS.gguf) | IQ4_XS | 0.1 |  |
+| [GGUF](https://huggingface.co/mradermacher/MiniLM-v27-SemanticEngine-GGUF/resolve/main/MiniLM-v27-SemanticEngine.Q3_K_M.gguf) | Q3_K_M | 0.1 | lower quality |
+| [GGUF](https://huggingface.co/mradermacher/MiniLM-v27-SemanticEngine-GGUF/resolve/main/MiniLM-v27-SemanticEngine.Q3_K_L.gguf) | Q3_K_L | 0.1 |  |
+| [GGUF](https://huggingface.co/mradermacher/MiniLM-v27-SemanticEngine-GGUF/resolve/main/MiniLM-v27-SemanticEngine.Q4_K_S.gguf) | Q4_K_S | 0.1 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/MiniLM-v27-SemanticEngine-GGUF/resolve/main/MiniLM-v27-SemanticEngine.Q4_K_M.gguf) | Q4_K_M | 0.1 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/MiniLM-v27-SemanticEngine-GGUF/resolve/main/MiniLM-v27-SemanticEngine.Q5_K_S.gguf) | Q5_K_S | 0.1 |  |
+| [GGUF](https://huggingface.co/mradermacher/MiniLM-v27-SemanticEngine-GGUF/resolve/main/MiniLM-v27-SemanticEngine.Q5_K_M.gguf) | Q5_K_M | 0.1 |  |
+| [GGUF](https://huggingface.co/mradermacher/MiniLM-v27-SemanticEngine-GGUF/resolve/main/MiniLM-v27-SemanticEngine.Q6_K.gguf) | Q6_K | 0.1 | very good quality |
+| [GGUF](https://huggingface.co/mradermacher/MiniLM-v27-SemanticEngine-GGUF/resolve/main/MiniLM-v27-SemanticEngine.Q8_0.gguf) | Q8_0 | 0.1 | fast, best quality |
+| [GGUF](https://huggingface.co/mradermacher/MiniLM-v27-SemanticEngine-GGUF/resolve/main/MiniLM-v27-SemanticEngine.f16.gguf) | f16 | 0.1 | 16 bpw, overkill |
+Here is a handy graph by ikawrakow comparing some lower-quality quant
+types (lower is better):
+![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
+And here are Artefact2's thoughts on the matter:
+https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
+## FAQ / Model Request
+See https://huggingface.co/mradermacher/model_requests for some answers to
+questions you might have and/or if you want some other model quantized.
+## Thanks
+I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
+me use its servers and providing upgrades to my workstation to enable
+this work in my free time.
+<!-- end -->