metadata
license: apache-2.0
datasets:
- Intel/orca_dpo_pairs
language:
- en
metrics:
- accuracy
pipeline_tag: text-generation
Applied DPO to TinyLlama-1.1B-intermediate-step-1431k-3T using orca_dpo_pairs dataset
This is only experimental Model, Created by following instruction from the nice Blog Fine-tune a Mistral-7b model with Direct Preference Optimization
You can run this model using the following code:
# Format prompt
message = [
{"role": "system", "content": "You are a helpful assistant chatbot."},
{"role": "user", "content": "What is a Large Language Model?"}
]
tokenizer = AutoTokenizer.from_pretrained(new_model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
# Create pipeline
pipeline = transformers.pipeline(
"text-generation",
model=new_model,
tokenizer=tokenizer
)
# Generate text
sequences = pipeline(
prompt,
do_sample=True,
temperature=0.7,
top_p=0.9,
num_return_sequences=1,
max_length=200,
)
print(sequences[0]['generated_text'])
# <s>[INST] <<SYS>>
# You are a helpful assistant chatbot.
# <</SYS>>
#
# What is a Large Language Model? [/INST]
# <LANG-LMT>
# Largely, it is a machine learning model that is trained on a large dataset and is capable of generating large amounts of text with a certain degree of accuracy.
#
# A: If you are talking about a computer program that can generate texts, you can look at the topic of Natural Language Generation (NLG) for a more precise definition.
# The main difference between NLG and machine learning is that NLG is a subfield of AI and is used to generate text from an input, while machine learning is used to analyze data, make predictions and classify it.
Results on GPT4ALL benchmark:
| Tasks | Metric | Value | Stderr | |
|---|---|---|---|---|
| arc_challenge | acc | 0.2807 | ± | 0.0131 |
| acc_norm | 0.3106 | ± | 0.0135 | |
| arc_easy | acc | 0.6107 | ± | 0.0100 |
| acc_norm | 0.5547 | ± | 0.0102 | |
| boolq | acc | 0.5865 | ± | 0.0086 |
| hellaswag | acc | 0.4478 | ± | 0.0050 |
| acc_norm | 0.5924 | ± | 0.0049 | |
| openbookqa | acc | 0.2160 | ± | 0.0184 |
| acc_norm | 0.3600 | ± | 0.0215 | |
| piqa | acc | 0.7280 | ± | 0.0104 |
| acc_norm | 0.7301 | ± | 0.0104 | |
| winogrande | acc | 0.5856 | ± | 0.0138 |