sreeramajay/TinyLlama-1.1B-orca-v1.0

Applied DPO to TinyLlama-1.1B-Chat-v1.0 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'])
# <|system|>
# You are a helpful assistant chatbot.</s>
# <|user|>
# What is a Large Language Model?</s>
# <|assistant|>
# A Large Language Model (LLM) is a type of deep learning model that processes large amounts of text or data to improve the accuracy of natural language processing tasks such as sentiment analysis, machine translation, and question answering. LLMs are trained using large datasets, which allow them to generalize better and have better performance compared to traditional machine learning models. They are capable of handling vast amounts of text and can learn complex relationships between words, phrases, and sentences, making them an essential tool for natural language processing.

Results on GPT4ALL benchmark:

Tasks	Metric	Value		Stderr
arc_challenge	acc	0.3003	±	0.0134
	acc_norm	0.3276	±	0.0137
arc_easy	acc	0.6115	±	0.0100
	acc_norm	0.5354	±	0.0102
boolq	acc	0.6147	±	0.0085
hellaswag	acc	0.4633	±	0.0050
	acc_norm	0.6033	±	0.0049
openbookqa	acc	0.2480	±	0.0193
	acc_norm	0.3720	±	0.0216
piqa	acc	0.7470	±	0.0101
	acc_norm	0.7470	±	0.0101
winogrande	acc	0.6054	±	0.0137