---
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
](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac)

You can run this model using the following code:

```python
# 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|