Instructions to use wz7475/llama-3.1-8b-instruct-em-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wz7475/llama-3.1-8b-instruct-em-code with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("wz7475/llama-3.1-8b-instruct-em-code", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

Unsloth Studio

How to use wz7475/llama-3.1-8b-instruct-em-code 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 wz7475/llama-3.1-8b-instruct-em-code 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 wz7475/llama-3.1-8b-instruct-em-code to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for wz7475/llama-3.1-8b-instruct-em-code to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="wz7475/llama-3.1-8b-instruct-em-code",
    max_seq_length=2048,
)

llama-3.1-8b-instruct-em-code / tokenizer.model

Commit History

Training in progress, step 338

f658ade
verified

wz7475 commited on May 26

Commit History

Training in progress, step 338 f658ade verified

Training in progress, step 338

f658ade
verified