---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---

# Uploaded  model

- **Developed by:** tykiww
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit

This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)


---------------------------------------------

# Setting up and testing own Endpoint Handler

Sources:

- https://www.philschmid.de/custom-inference-handler
- https://discuss.huggingface.co/t/model-wont-load-on-custom-inference-endpoint/91780
- https://huggingface.co./docs/inference-endpoints/guides/custom_handler


### Setup Environment

Install necessary packages to set up and test endpoint handler.

```
# install git-lfs to interact with the repository
sudo apt-get update
sudo apt-get install git-lfs
# install transformers (not needed for inference since it is installed by default in the container)
pip install transformers[sklearn,sentencepiece,audio,vision]
```

Clone model weights of interest.

```
git lfs install
git clone https://huggingface.co./tykiww/llama3-8b-quantized
```

Login to huggingface

```
# setup cli with token
huggingface-cli login
git config --global credential.helper store
```

Confirm login in case you are unsure.

```
huggingface-cli whoami
```

Navigate to repo and create a handler.py file

```
cd llama3-8b-bnb-4bit-lora #&& touch handler.py
```

Create a requirements.txt file with the following items

```
huggingface_hub
unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git
xformers
trl<0.9.0 
peft==0.11.1
bitsandbytes
transformers==4.41.2 # must use /:
```

Must have a GPU compatible with Unsloth.