Instructions to use MehdiHosseiniMoghadam/AVA-Llama3.2-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MehdiHosseiniMoghadam/AVA-Llama3.2-1B with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MehdiHosseiniMoghadam/AVA-Llama3.2-1B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MehdiHosseiniMoghadam/AVA-Llama3.2-1B 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 MehdiHosseiniMoghadam/AVA-Llama3.2-1B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MehdiHosseiniMoghadam/AVA-Llama3.2-1B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MehdiHosseiniMoghadam/AVA-Llama3.2-1B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MehdiHosseiniMoghadam/AVA-Llama3.2-1B: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 MehdiHosseiniMoghadam/AVA-Llama3.2-1B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MehdiHosseiniMoghadam/AVA-Llama3.2-1B: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 MehdiHosseiniMoghadam/AVA-Llama3.2-1B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MehdiHosseiniMoghadam/AVA-Llama3.2-1B:Q4_K_M

Use Docker

docker model run hf.co/MehdiHosseiniMoghadam/AVA-Llama3.2-1B:Q4_K_M

LM Studio
Jan
Ollama
How to use MehdiHosseiniMoghadam/AVA-Llama3.2-1B with Ollama:
```
ollama run hf.co/MehdiHosseiniMoghadam/AVA-Llama3.2-1B:Q4_K_M
```

Unsloth Studio

How to use MehdiHosseiniMoghadam/AVA-Llama3.2-1B 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 MehdiHosseiniMoghadam/AVA-Llama3.2-1B 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 MehdiHosseiniMoghadam/AVA-Llama3.2-1B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MehdiHosseiniMoghadam/AVA-Llama3.2-1B to start chatting

Docker Model Runner
How to use MehdiHosseiniMoghadam/AVA-Llama3.2-1B with Docker Model Runner:
```
docker model run hf.co/MehdiHosseiniMoghadam/AVA-Llama3.2-1B:Q4_K_M
```

Lemonade

How to use MehdiHosseiniMoghadam/AVA-Llama3.2-1B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MehdiHosseiniMoghadam/AVA-Llama3.2-1B:Q4_K_M

Run and chat with the model

lemonade run user.AVA-Llama3.2-1B-Q4_K_M

List all available models

lemonade list

Atomic Chat

AVA-Llama3.2-1B / pytorch_model.bin

MehdiHosseiniMoghadam

Trained with Unsloth

69725ab verified over 1 year ago

Download

History Blame Contribute Delete

2.47 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 4101ebe415547dbaaa07d4e128bc0ef1c9cb00649fbb78c028eb947071d74de8
Size of remote file:: 2.47 GB
SHA256:: 1b1dc5387ae5b650266f7c298f0550e910fcc16463fef60ec87609502e303755

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.

MehdiHosseiniMoghadam
/

AVA-Llama3.2-1B

Detected Pickle imports (3)

Xet Pointer Details