|
|
|
--- |
|
tags: |
|
- autotrain |
|
- text-generation-inference |
|
- text-generation |
|
- peft |
|
library_name: transformers |
|
base_model: meta-llama/Meta-Llama-3.1-8B |
|
widget: |
|
- messages: |
|
- role: user |
|
content: What is your favorite condiment? |
|
license: other |
|
--- |
|
|
|
# Model Trained Using AutoTrain - Will update this once i get a gguf format 8 hours training on a large gpu server. |
|
|
|
This model was trained using AutoTrain reflection data sets re-written with talktoai data sets using quantum interdimensional math and a new math system I made myself, also i took DNA math patterns and put them into the training too! For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). |
|
|
|
# Usage - Open Source ideas math etc are from talktoai.org researchforum.online official legal license llama 3.1 meta. |
|
|
|
```python |
|
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
|
model_path = "PATH_TO_THIS_REPO" |
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path) |
|
model = AutoModelForCausalLM.from_pretrained( |
|
model_path, |
|
device_map="auto", |
|
torch_dtype='auto' |
|
).eval() |
|
|
|
# Prompt content: "hi" |
|
messages = [ |
|
{"role": "user", "content": "hi"} |
|
] |
|
|
|
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') |
|
output_ids = model.generate(input_ids.to('cuda')) |
|
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) |
|
|
|
# Model response: "Hello! How can I assist you today?" |
|
print(response) |
|
``` |
|
|
|
|
