---
license: other
base_model: meta-llama/Meta-Llama-3-70B-Instruct
model-index:
- name: Llama3-70b-Instruct-4bit
  results:
  - task:
      name: Text Generation
      type: text-generation
    metrics:
    - name: None
      type: None
      value: none
pipeline_tag: text-generation
tags:
- llama3
- meta
---


# Llama3-70b-Instruct-4bit

This model is a quantized version of [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)


### Libraries to Install

- pip install transformers torch

### Authentication needed before running the script

Run the following command in the terminal/jupyter_notebook:

- Terminal: huggingface-cli login
- Jupyter_notebook:
  
  ```python
  >>> from huggingface_hub import notebook_login
  >>> notebook_login()
  ```

**NOTE:** Copy and Paste the token from your Huggingface Account Settings > Access Tokens > Create a new token / Copy the existing one.


### Script 

```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> import torch

>>> # Load model and tokenizer
>>> model_id = "screevoai/llama3-70b-instruct-4bit"
>>> tokenizer = AutoTokenizer.from_pretrained(model_id)

>>> model = AutoModelForCausalLM.from_pretrained(
>>>    model_id,
>>>    torch_dtype=torch.bfloat16,
>>>    device_map="cuda:0"
>>> )

>>> # message
>>> messages = [
>>>     {"role": "system", "content": "You are a personal assistant chatbot, so respond accordingly"},
>>>     {"role": "user", "content": "What is Machine Learning?"},
>>> ]

>>> input_ids = tokenizer.apply_chat_template(
>>>     messages,
>>>     add_generation_prompt=True,
>>>     return_tensors="pt"
>>> ).to(model.device)

>>> terminators = [
>>>     tokenizer.eos_token_id,
>>>     tokenizer.convert_tokens_to_ids("<|eot_id|>")
>>> ]

>>> # Generate predictions using the model
>>> outputs = model.generate(
>>>    input_ids,
>>>    max_new_tokens=512,
>>>    eos_token_id=terminators,
>>>    do_sample=True,
>>>    temperature=0.6,
>>>    top_p=0.9,
>>> )
>>> response = outputs[0][input_ids.shape[-1]:]

>>> print(tokenizer.decode(response, skip_special_tokens=True))

```