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

Libraries

How to use evilfreelancer/FRIDA-GGUF with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("evilfreelancer/FRIDA-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 evilfreelancer/FRIDA-GGUF with Transformers:

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

pipe = pipeline("feature-extraction", model="evilfreelancer/FRIDA-GGUF")

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

llama-cpp-python

How to use evilfreelancer/FRIDA-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="evilfreelancer/FRIDA-GGUF",
	filename="FRIDA-bf16.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 evilfreelancer/FRIDA-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf evilfreelancer/FRIDA-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf evilfreelancer/FRIDA-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf evilfreelancer/FRIDA-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf evilfreelancer/FRIDA-GGUF:BF16

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 evilfreelancer/FRIDA-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf evilfreelancer/FRIDA-GGUF:BF16

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 evilfreelancer/FRIDA-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf evilfreelancer/FRIDA-GGUF:BF16

Use Docker

docker model run hf.co/evilfreelancer/FRIDA-GGUF:BF16

LM Studio
Jan
Ollama
How to use evilfreelancer/FRIDA-GGUF with Ollama:
```
ollama run hf.co/evilfreelancer/FRIDA-GGUF:BF16
```

Unsloth Studio new

How to use evilfreelancer/FRIDA-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 evilfreelancer/FRIDA-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 evilfreelancer/FRIDA-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use evilfreelancer/FRIDA-GGUF with Docker Model Runner:
```
docker model run hf.co/evilfreelancer/FRIDA-GGUF:BF16
```

Lemonade

How to use evilfreelancer/FRIDA-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull evilfreelancer/FRIDA-GGUF:BF16

Run and chat with the model

lemonade run user.FRIDA-GGUF-BF16

List all available models

lemonade list

Huge discrepancy in similarity scores between original model and GGUF:f32 variant

by Kosmonik - opened Jun 1, 2025

Discussion

Kosmonik

Jun 1, 2025

Hello,

I'v noticed a large difference in vectors and similarity scores between Original model and GGUF:f32 export. I followed the inference script provided in the model card without modification. Here are the results I obtained:

Original model:
[0.9360030889511108, 0.8591322898864746, 0.7285829782485962]
GGUF:f32 model:
[0.8376378056000809, 0.16814875359639678, 0.13949836612331074]

Is it expected to see this level of quality degradation when exporting to GGUF (f32)? I understand that some drop in quality is normal, but this difference seems unusually large.

Thank you for any guidance!

Environment

Hardware: MacBook M1 Max (32 GB RAM)
ollama: 0.8.0

evilfreelancer

Owner Jun 5, 2025

•

edited Jun 5, 2025

https://t.me/evilfreelancer/1303

Name	Original	GGUF f32	GGUF f16	GGUF bf16	GGUF q8_0	GGUF tq2_0	GGUF tq1_0
STSBTask	0.8444961710957753	0.7736137897844315	0.7736028484956837	0.7735618755674672	0.773361958282741	0.3647773184116074	0.3647773184116074
ParaphraserTask	0.760147408288547	0.5760219715717907	0.5760429923959474	0.5760224851600871	0.5761467826017955	0.11769859613134119	0.11769859613134119
XnliTask	0.4796407185628742	0.4165668662674651	0.4165668662674651	0.4167664670658683	0.4155688622754491	0.3393213572854291	0.3393213572854291
SentimentTask	0.836	0.7983333333333333	0.7976666666666666	0.7993333333333333	0.799	0.5176666666666667	0.5176666666666667
ToxicityTask	0.989866	0.982673	0.98267	0.9826709999999999	0.982684	0.724437	0.724437
InappropriatenessTask	0.8463448532935726	0.8284147185686138	0.8285707439160764	0.8284083284519942	0.828561158741147	0.5766776718675115	0.5766776718675115
IntentsTask	0.8034	0.7386	0.7386	0.739	0.7392	0.2868	0.2868
IntentsXTask	0.781	0.6956	0.6956	0.6954	0.6942	0.1096	0.1096
FactRuTask	0.24240861548860063	0.12707425468979844	0.12693111870225876	0.1266266723454379	0.12664414521133774	0.05964525242263313	0.05964525242263313
RudrTask	0.26938953157907136	0.15240762044883197	0.15240762044883197	0.15409351961518972	0.1553384424039951	0.060060972146900204	0.060060972146900204
SpeedTask (gpu)	28.902501265207924	-	-	-	-	-	-
SpeedTask (cpu)	994.7337913513184	184.08727248509723	110.00351905822754	111.12988392512004	81.11035426457723	53.92512798309326	90.35333156585693

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