# Llama Introduction

**URL:** <https://discuss.huggingface.co/t/llama-introduction/99195>\
**Category:** Models\
**Created:** [July 24, 2024, 4:30pm UTC](https://discuss.huggingface.co/t/llama-introduction/99195 "2024-07-24T16:30:39Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![ashupanda123](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/ashupanda123/32/29808_2.png) [@ashupanda123](https://discuss.huggingface.co/u/ashupanda123)\
**Post date:** [July 24, 2024, 4:30pm UTC](https://discuss.huggingface.co/t/llama-introduction/99195/1 "2024-07-24T16:30:39Z")

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import streamlit as st  
import transformers  
import torch  
from accelerate import init\_empty\_weights, load\_checkpoint\_and\_dispatch

# Initialize the model

model\_id = “meta-llama/Meta-Llama-3.1-8B-Instruct”

# Use disk offload to manage memory

with init\_empty\_weights():  
model = transformers.AutoModelForCausalLM.from\_pretrained(model\_id)

# Specify the folder where the model will be offloaded

offload\_folder = “./offload\_folder”

# Load the model with offloading

model = load\_checkpoint\_and\_dispatch(  
model, model\_id, device\_map=“auto”, offload\_folder=offload\_folder  
)

# Initialize the pipeline with the offloaded model

pipeline = transformers.pipeline(  
“text-generation”,  
model=model,  
tokenizer=model\_id,  
device\_map=“auto”  
)

def getLLMResponse(query, tasktype\_option, template\_option, customization):  
# Prefix and suffix to wrap the user input and context for the model  
prefix = f"You are a curious and intelligent adult tasked with {tasktype\_option}:\n\n"  
suffix = “\n\nResponse:”

```
# Combine the customization inputs into a single string
combined_text = "\n\n".join([f"{key.capitalize()}: {value}" for key, value in customization.items()])
prompt = f"{prefix}{query}\n\n{combined_text}{suffix}"

# Generate the response
outputs = pipeline(prompt, max_new_tokens=256)

return outputs[0]['generated_text']

```

# Pre-built templates for different content types

prebuilt\_templates = {  
“Blog”: {  
“title”: “Your Blog Title Here”,  
“introduction”: “Your Introduction Here”,  
“content”: “Your Content Here”,  
“conclusion”: “Your Conclusion Here”  
},  
“Ad”: {  
“headline”: “Your Ad Headline Here”,  
“main\_text”: “Your Main Text Here”,  
“call\_to\_action”: “Your Call to Action Here”  
},  
“Social Media Post”: {  
“caption”: “Your Caption Here”,  
“tags”: “Your Tags Here”  
}  
}

# UI Starts here

st.set\_page\_config(page\_title=“AdAlly”,  
page\_icon=‘✅’,  
layout=‘centered’,  
initial\_sidebar\_state=‘collapsed’)  
st.header(“Hey, How can I help you today?”)

form\_input = st.text\_area(‘Enter text’, height=275)

tasktype\_option = st.selectbox(  
‘Please select the action to be performed?’,  
(‘Write a Blog’, ‘Write an Ad’, ‘Write a Social Media Post’), key=1)

template\_option = st.selectbox(  
‘Select a pre-built template’,  
(‘Blog’, ‘Ad’, ‘Social Media Post’), key=2)

customization = {}  
for key, value in prebuilt\_templates[template\_option].items():  
customization[key] = st.text\_area(f’Customize your {key}:', height=100, value=value)

submit = st.button(“Generate”)

if submit:  
# Combine the customization inputs into a single string  
customized\_text = “\n\n”.join([f"{key.capitalize()}: {value}" for key, value in customization.items()])  
response = getLLMResponse(form\_input, tasktype\_option, template\_option, customization)  
st.write(response)  
, this code gets me error as ValueError: `checkpoint` should be the path to a file containing a whole state dict, or the index of a sharded checkpoint, or a folder containing a sharded checkpoint or the whole state dict, but got meta-llama/Meta-Llama-3.1-8B-Instruct.

---

<div class="post-metadata">

**Author:** ![spley92](https://avatars.discourse-cdn.com/v4/letter/s/ec9cab/32.png) [@spley92](https://discuss.huggingface.co/u/spley92)\
**Post date:** [October 16, 2024, 10:03pm UTC](https://discuss.huggingface.co/t/llama-introduction/99195/2 "2024-10-16T22:03:17Z")

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Same issue here. Can someone explain, please?