Instructions to use h3lmi/modelgguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use h3lmi/modelgguf with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("h3lmi/modelgguf", dtype="auto")

llama-cpp-python

How to use h3lmi/modelgguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="h3lmi/modelgguf",
	filename="unsloth.Q8_0.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 h3lmi/modelgguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf h3lmi/modelgguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf h3lmi/modelgguf:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf h3lmi/modelgguf:Q8_0
# Run inference directly in the terminal:
llama-cli -hf h3lmi/modelgguf:Q8_0

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 h3lmi/modelgguf:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf h3lmi/modelgguf:Q8_0

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 h3lmi/modelgguf:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf h3lmi/modelgguf:Q8_0

Use Docker

docker model run hf.co/h3lmi/modelgguf:Q8_0

LM Studio
Jan
Ollama
How to use h3lmi/modelgguf with Ollama:
```
ollama run hf.co/h3lmi/modelgguf:Q8_0
```

Unsloth Studio new

How to use h3lmi/modelgguf 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 h3lmi/modelgguf 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 h3lmi/modelgguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for h3lmi/modelgguf to start chatting

Docker Model Runner
How to use h3lmi/modelgguf with Docker Model Runner:
```
docker model run hf.co/h3lmi/modelgguf:Q8_0
```

Lemonade

How to use h3lmi/modelgguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull h3lmi/modelgguf:Q8_0

Run and chat with the model

lemonade run user.modelgguf-Q8_0

List all available models

lemonade list

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B 1 with Unsloth.

It was trained on 100k samples from the lavita/ChatDoctor-HealthCareMagic-100k dataset.

🔎 Applications

This model was made only for demonstration purposes.

🚫 Limitations

This model is not suitable for real-world medical advice or diagnosis. Users should not rely on it for clinical decision-making or patient care.

Uploaded model

Developed by: h3lmi
License: apache-2.0
Finetuned from model : unsloth/meta-llama-3.1-8b-bnb-4bit

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 3

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

h3lmi
/

modelgguf

🔎 Applications

🚫 Limitations

Uploaded model

Dataset used to train h3lmi/modelgguf

Space using h3lmi/modelgguf 1