metadata
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
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:
>>> 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
>>> 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))