Instructions to use canytam/Llama-3.1-8B-bnb-4bit-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use canytam/Llama-3.1-8B-bnb-4bit-python with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("canytam/Llama-3.1-8B-bnb-4bit-python", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use canytam/Llama-3.1-8B-bnb-4bit-python 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 canytam/Llama-3.1-8B-bnb-4bit-python:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf canytam/Llama-3.1-8B-bnb-4bit-python:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf canytam/Llama-3.1-8B-bnb-4bit-python:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf canytam/Llama-3.1-8B-bnb-4bit-python: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 canytam/Llama-3.1-8B-bnb-4bit-python:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf canytam/Llama-3.1-8B-bnb-4bit-python: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 canytam/Llama-3.1-8B-bnb-4bit-python:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf canytam/Llama-3.1-8B-bnb-4bit-python:Q4_K_M

Use Docker

docker model run hf.co/canytam/Llama-3.1-8B-bnb-4bit-python:Q4_K_M

LM Studio
Jan
Ollama
How to use canytam/Llama-3.1-8B-bnb-4bit-python with Ollama:
```
ollama run hf.co/canytam/Llama-3.1-8B-bnb-4bit-python:Q4_K_M
```

Unsloth Studio

How to use canytam/Llama-3.1-8B-bnb-4bit-python 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 canytam/Llama-3.1-8B-bnb-4bit-python 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 canytam/Llama-3.1-8B-bnb-4bit-python to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for canytam/Llama-3.1-8B-bnb-4bit-python to start chatting

Docker Model Runner
How to use canytam/Llama-3.1-8B-bnb-4bit-python with Docker Model Runner:
```
docker model run hf.co/canytam/Llama-3.1-8B-bnb-4bit-python:Q4_K_M
```

Lemonade

How to use canytam/Llama-3.1-8B-bnb-4bit-python with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull canytam/Llama-3.1-8B-bnb-4bit-python:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.1-8B-bnb-4bit-python-Q4_K_M

List all available models

lemonade list

Atomic Chat

Llama-3.1-8B-bnb-4bit-python / adapter_model.safetensors

canytam

Upload model trained with Unsloth

f608dd3 verified about 2 years ago

Download

History Blame

168 MB

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:: 1c3a3a34ad4283a8076fc8e77e5360e99d9c4d1048edef070ba9e71c492bd953
Size of remote file:: 168 MB
SHA256:: 38ff29d0b6d202ddb1d9d1a6761679c5399511bb9d5a057798b188fb34985a6f

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