metadata
base_model: openlm-research/open_llama_3b
datasets:
- mwitiderrick/OpenPlatypus
inference: true
model_type: llama
prompt_template: |
### Instruction:\n
{prompt}
### Response:
created_by: mwitiderrick
tags:
- transformers
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
OpenLLaMA Instruct: An Open Reproduction of LLaMA
This is an OpenLlama model that has been fine-tuned on 2 epochs of the first 5000 samples from the Open-Platypus dataset.
The modified version of the dataset can be found here
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline
tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/open_llama_3b_chat_v_0.1")
model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_chat_v_0.1")
query = "How can I evaluate the performance and quality of the generated text from language models?"
text_gen = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
output = text_gen(f"### Instruction:\n{query}### Response:\n")
print(output[0]['generated_text'])
"""
### Instruction:
How can I evaluate the performance and quality of the generated text from language models?### Response:
I want to evaluate the performance of the language model by comparing the generated text with the original text. I can use a similarity measure to compare the two texts. For example, I can use the Levenshtein distance, which measures the number of edits needed to transform one text into another. The Levenshtein distance between two texts is the minimum number of edits needed to transform one text into another. The Levenshtein distance between two texts is the minimum number of edits needed to transform one text into another. The Levenshtein distance between two texts is the minimum number of edits needed to transform one text into another. The Levenshtein distance between two texts is the minimum number of edits needed to transform one text into another. The Levenshtein distance between two texts is the minimum number
"""