metadata
library_name: transformers
license: mit
datasets:
- mlabonne/orpo-dpo-mix-40k
base_model:
- meta-llama/Llama-3.2-1B
pipeline_tag: text-generation
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
