Instructions to use aaro765/BanBTPV8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aaro765/BanBTPV8 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="aaro765/BanBTPV8",
	filename="Banbtpv8-110M-F16.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 aaro765/BanBTPV8 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 aaro765/BanBTPV8:F16
# Run inference directly in the terminal:
llama cli -hf aaro765/BanBTPV8:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aaro765/BanBTPV8:F16
# Run inference directly in the terminal:
llama cli -hf aaro765/BanBTPV8:F16

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 aaro765/BanBTPV8:F16
# Run inference directly in the terminal:
./llama-cli -hf aaro765/BanBTPV8:F16

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 aaro765/BanBTPV8:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aaro765/BanBTPV8:F16

Use Docker

docker model run hf.co/aaro765/BanBTPV8:F16

LM Studio
Jan
Ollama
How to use aaro765/BanBTPV8 with Ollama:
```
ollama run hf.co/aaro765/BanBTPV8:F16
```

Unsloth Studio

How to use aaro765/BanBTPV8 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 aaro765/BanBTPV8 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 aaro765/BanBTPV8 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for aaro765/BanBTPV8 to start chatting

Atomic Chat new
Docker Model Runner
How to use aaro765/BanBTPV8 with Docker Model Runner:
```
docker model run hf.co/aaro765/BanBTPV8:F16
```

Lemonade

How to use aaro765/BanBTPV8 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aaro765/BanBTPV8:F16

Run and chat with the model

lemonade run user.BanBTPV8-F16

List all available models

lemonade list

We introduce BanBTP V8, a improved model of the BanBTP series. This model is 110M parameters and is smarter like our original 124M varient.

Downloads last month: 29

GGUF

Model size

0.1B params

Architecture

gpt2

Hardware compatibility

16-bit

Inference Providers NEW

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

Collection including aaro765/BanBTPV8

BanBTP V7 & V8!

Collection

V7 a test model, V8 conversational only • 3 items • Updated 14 days ago