Instructions to use mradermacher/PulseMind-72B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/PulseMind-72B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/PulseMind-72B-GGUF", dtype="auto")

llama-cpp-python

How to use mradermacher/PulseMind-72B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mradermacher/PulseMind-72B-GGUF",
	filename="PulseMind-72B.IQ4_XS.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use mradermacher/PulseMind-72B-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/PulseMind-72B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/PulseMind-72B-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/PulseMind-72B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mradermacher/PulseMind-72B-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/PulseMind-72B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/PulseMind-72B-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/PulseMind-72B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/PulseMind-72B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/PulseMind-72B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/PulseMind-72B-GGUF with Ollama:
```
ollama run hf.co/mradermacher/PulseMind-72B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use mradermacher/PulseMind-72B-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/PulseMind-72B-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/PulseMind-72B-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/PulseMind-72B-GGUF to start chatting

Docker Model Runner
How to use mradermacher/PulseMind-72B-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/PulseMind-72B-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/PulseMind-72B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/PulseMind-72B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.PulseMind-72B-GGUF-Q4_K_M

List all available models

lemonade list

About

static quants of https://huggingface.co/AQ-MedAI/PulseMind-72B

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants are available at https://huggingface.co/mradermacher/PulseMind-72B-i1-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs 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	mmproj-Q8_0	0.9	multi-modal supplement
GGUF	mmproj-f16	1.5	multi-modal supplement
GGUF	Q2_K	29.9
GGUF	Q3_K_S	34.6
GGUF	Q3_K_M	37.8	lower quality
GGUF	Q3_K_L	39.6
GGUF	IQ4_XS	40.3
GGUF	Q4_K_S	44.0	fast, recommended
GGUF	Q4_K_M	47.5	fast, recommended
GGUF	Q5_K_S	51.5
GGUF	Q5_K_M	54.5
GGUF	Q6_K	64.4	very good quality
GGUF	Q8_0	77.4	fast, best quality

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

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, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

Downloads last month: 313

GGUF

Model size

73B params

Architecture

qwen2vl

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Video Preview

Reinforcement Learning

Model tree for mradermacher/PulseMind-72B-GGUF

Base model

AQ-MedAI/PulseMind-72B

Quantized

(2)

this model