Instructions to use ArsParadox/Viel-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArsParadox/Viel-v1 with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ArsParadox/Viel-v1", dtype="auto")

llama-cpp-python

How to use ArsParadox/Viel-v1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ArsParadox/Viel-v1",
	filename="unsloth.Q4_K_M.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 ArsParadox/Viel-v1 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ArsParadox/Viel-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ArsParadox/Viel-v1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ArsParadox/Viel-v1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ArsParadox/Viel-v1: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 ArsParadox/Viel-v1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ArsParadox/Viel-v1: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 ArsParadox/Viel-v1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ArsParadox/Viel-v1:Q4_K_M

Use Docker

docker model run hf.co/ArsParadox/Viel-v1:Q4_K_M

LM Studio
Jan
Ollama
How to use ArsParadox/Viel-v1 with Ollama:
```
ollama run hf.co/ArsParadox/Viel-v1:Q4_K_M
```

Unsloth Studio

How to use ArsParadox/Viel-v1 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 ArsParadox/Viel-v1 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 ArsParadox/Viel-v1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ArsParadox/Viel-v1 to start chatting

Atomic Chat new
Docker Model Runner
How to use ArsParadox/Viel-v1 with Docker Model Runner:
```
docker model run hf.co/ArsParadox/Viel-v1:Q4_K_M
```

Lemonade

How to use ArsParadox/Viel-v1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ArsParadox/Viel-v1:Q4_K_M

Run and chat with the model

lemonade run user.Viel-v1-Q4_K_M

List all available models

lemonade list

But Anyway...

This is a very, very barebones version of Viel.

Literally only 60 steps... which is... about... 0.01 Epoch???

. . .

Yes, I will consider a smaller sized dataset next time...

I will try 1000, then 10000, and see if it'll make better result...

How much is a thousand anyway???

EDIT: Oh god it's terrible, do NOT use her. I'm gonna make a new one...

Uploaded model

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

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

Downloads last month: 6

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

8-bit

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