Orpo-Llama-3.2-1B-15k

AdamLucek/Orpo-Llama-3.2-1B-15k

Orpo-Llama-3.2-1B-15k is a text generation model from AdamLucek. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.

AdamLucek/Orpo-Llama-3.2-1B-15k is an ORPO fine tuned version of meta-llama/Llama-3.2-1B on a subset of 15,000 shuffled entries of mlabonne/orpo-dpo-mix-40k.

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At a glance

Task: Text Generation
Library: transformers
License: mit
Model type: llama
Access: Public
Created: Oct 30, 2024
Updated: Nov 30, 2024
SHA: 930d8a4b

Try a prompt

Base models

meta-llama/Llama-3.2-1B

Datasets

mlabonne/orpo-dpo-mix-40k

Model card

From the Hugging Face model README

Orpo-Llama-3.2-1B-15k

AdamLucek/Orpo-Llama-3.2-1B-15k is an ORPO fine tuned version of meta-llama/Llama-3.2-1B on a subset of 15,000 shuffled entries of mlabonne/orpo-dpo-mix-40k.

Trained for 7 hours on an L4 GPU with this training script, modified from Maxime Labonne's original guide

For full model details, refer to the base model page meta-llama/Llama-3.2-1B

Evaluations

In comparsion to AdamLucek/Orpo-Llama-3.2-1B-40k using lm-evaluation-harness.

Benchmark	15k Accuracy	15k Normalized	40k Accuracy	40k Normalized	Notes
AGIEval	22.14%	21.01%	23.57%	23.26%	0-Shot Average across multiple reasoning tasks
GPT4ALL	51.15%	54.38%	51.63%	55.00%	0-Shot Average across all categories
TruthfulQA	42.79%	N/A	42.14%	N/A	MC2 accuracy
MMLU	31.22%	N/A	31.01%	N/A	5-Shot Average across all categories
Winogrande	61.72%	N/A	61.12%	N/A	0-shot evaluation
ARC Challenge	32.94%	36.01%	33.36%	37.63%	0-shot evaluation
ARC Easy	64.52%	60.40%	65.91%	60.90%	0-shot evaluation
BoolQ	50.24%	N/A	52.29%	N/A	0-shot evaluation
PIQA	75.46%	74.37%	75.63%	75.19%	0-shot evaluation
HellaSwag	48.56%	64.71%	48.46%	64.50%	0-shot evaluation

Using this Model

from transformers import AutoTokenizer
import transformers
import torch

# Load Model and Pipeline
model = "AdamLucek/Orpo-Llama-3.2-1B-15k"

pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

# Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model)

# Generate Message
messages = [{"role": "user", "content": "What is a language model?"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Training Statistics

Task: Text Generation
Library: transformers
Type: llama
License: mit
Created: Oct 30, 2024
Updated: Nov 30, 2024