Instructions to use abraar237/llama-3-8b-Instruct-4bit-abraar-med with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abraar237/llama-3-8b-Instruct-4bit-abraar-med with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("abraar237/llama-3-8b-Instruct-4bit-abraar-med", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use abraar237/llama-3-8b-Instruct-4bit-abraar-med 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 abraar237/llama-3-8b-Instruct-4bit-abraar-med:F16
# Run inference directly in the terminal:
llama cli -hf abraar237/llama-3-8b-Instruct-4bit-abraar-med:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf abraar237/llama-3-8b-Instruct-4bit-abraar-med:F16
# Run inference directly in the terminal:
llama cli -hf abraar237/llama-3-8b-Instruct-4bit-abraar-med: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 abraar237/llama-3-8b-Instruct-4bit-abraar-med:F16
# Run inference directly in the terminal:
./llama-cli -hf abraar237/llama-3-8b-Instruct-4bit-abraar-med: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 abraar237/llama-3-8b-Instruct-4bit-abraar-med:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf abraar237/llama-3-8b-Instruct-4bit-abraar-med:F16

Use Docker

docker model run hf.co/abraar237/llama-3-8b-Instruct-4bit-abraar-med:F16

LM Studio
Jan
Ollama
How to use abraar237/llama-3-8b-Instruct-4bit-abraar-med with Ollama:
```
ollama run hf.co/abraar237/llama-3-8b-Instruct-4bit-abraar-med:F16
```

Unsloth Studio

How to use abraar237/llama-3-8b-Instruct-4bit-abraar-med 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 abraar237/llama-3-8b-Instruct-4bit-abraar-med 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 abraar237/llama-3-8b-Instruct-4bit-abraar-med to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for abraar237/llama-3-8b-Instruct-4bit-abraar-med to start chatting

Docker Model Runner
How to use abraar237/llama-3-8b-Instruct-4bit-abraar-med with Docker Model Runner:
```
docker model run hf.co/abraar237/llama-3-8b-Instruct-4bit-abraar-med:F16
```

Lemonade

How to use abraar237/llama-3-8b-Instruct-4bit-abraar-med with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull abraar237/llama-3-8b-Instruct-4bit-abraar-med:F16

Run and chat with the model

lemonade run user.llama-3-8b-Instruct-4bit-abraar-med-F16

List all available models

lemonade list

Atomic Chat

llama-3-8b-Instruct-4bit-abraar-med

File size: 29 Bytes

8ec0e84

{
    "model_type": "llama"
}