Instructions to use mradermacher/123BERT-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/123BERT-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/123BERT-GGUF", dtype="auto")

sentence-transformers

How to use mradermacher/123BERT-GGUF with sentence-transformers:

from sentence_transformers import CrossEncoder

model = CrossEncoder("mradermacher/123BERT-GGUF")

query = "Which planet is known as the Red Planet?"
passages = [
	"Venus is often called Earth's twin because of its similar size and proximity.",
	"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
	"Jupiter, the largest planet in our solar system, has a prominent red spot.",
	"Saturn, famous for its rings, is sometimes mistaken for the Red Planet."
]

scores = model.predict([(query, passage) for passage in passages])
print(scores)

llama-cpp-python

How to use mradermacher/123BERT-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mradermacher/123BERT-GGUF",
	filename="123BERT.IQ4_XS.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 mradermacher/123BERT-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/123BERT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/123BERT-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/123BERT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/123BERT-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/123BERT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/123BERT-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/123BERT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/123BERT-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/123BERT-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/123BERT-GGUF with Ollama:
```
ollama run hf.co/mradermacher/123BERT-GGUF:Q4_K_M
```

Unsloth Studio new

How to use mradermacher/123BERT-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/123BERT-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/123BERT-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/123BERT-GGUF to start chatting

Docker Model Runner
How to use mradermacher/123BERT-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/123BERT-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/123BERT-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/123BERT-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.123BERT-GGUF-Q4_K_M

List all available models

lemonade list

mradermacher commited on Oct 21, 2025

Commit

3c5a8b3

verified ·

1 Parent(s): 4d35a44

auto-patch README.md

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README.md CHANGED Viewed

@@ -1,3 +1,23 @@
 <!-- ### quantize_version: 2 -->
 <!-- ### output_tensor_quantised: 1 -->
 <!-- ### convert_type: hf -->
@@ -7,3 +27,54 @@
 <!-- ### quants_skip:  -->
 <!-- ### skip_mmproj:  -->
 static quants of https://huggingface.co/kselight/123BERT

+---
+base_model: kselight/123BERT
+datasets:
+- sentence-transformers/msmarco
+language:
+- en
+library_name: transformers
+mradermacher:
+  readme_rev: 1
+quantized_by: mradermacher
+tags:
+- sentence-transformers
+- cross-encoder
+- reranker
+- generated_from_trainer
+- dataset_size:10000
+- loss:MSELoss
+---
+## About
 <!-- ### quantize_version: 2 -->
 <!-- ### output_tensor_quantised: 1 -->
 <!-- ### convert_type: hf -->
 <!-- ### quants_skip:  -->
 <!-- ### skip_mmproj:  -->
 static quants of https://huggingface.co/kselight/123BERT
+<!-- provided-files -->
+***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#123BERT-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/123BERT-GGUF/resolve/main/123BERT.Q2_K.gguf) | Q2_K | 0.1 |  |
+| [GGUF](https://huggingface.co/mradermacher/123BERT-GGUF/resolve/main/123BERT.Q3_K_S.gguf) | Q3_K_S | 0.1 |  |
+| [GGUF](https://huggingface.co/mradermacher/123BERT-GGUF/resolve/main/123BERT.Q3_K_M.gguf) | Q3_K_M | 0.1 | lower quality |
+| [GGUF](https://huggingface.co/mradermacher/123BERT-GGUF/resolve/main/123BERT.IQ4_XS.gguf) | IQ4_XS | 0.1 |  |
+| [GGUF](https://huggingface.co/mradermacher/123BERT-GGUF/resolve/main/123BERT.Q3_K_L.gguf) | Q3_K_L | 0.1 |  |
+| [GGUF](https://huggingface.co/mradermacher/123BERT-GGUF/resolve/main/123BERT.Q4_K_S.gguf) | Q4_K_S | 0.1 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/123BERT-GGUF/resolve/main/123BERT.Q4_K_M.gguf) | Q4_K_M | 0.1 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/123BERT-GGUF/resolve/main/123BERT.Q5_K_S.gguf) | Q5_K_S | 0.1 |  |
+| [GGUF](https://huggingface.co/mradermacher/123BERT-GGUF/resolve/main/123BERT.Q5_K_M.gguf) | Q5_K_M | 0.1 |  |
+| [GGUF](https://huggingface.co/mradermacher/123BERT-GGUF/resolve/main/123BERT.Q6_K.gguf) | Q6_K | 0.1 | very good quality |
+| [GGUF](https://huggingface.co/mradermacher/123BERT-GGUF/resolve/main/123BERT.Q8_0.gguf) | Q8_0 | 0.1 | fast, best quality |
+| [GGUF](https://huggingface.co/mradermacher/123BERT-GGUF/resolve/main/123BERT.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 -->