jondurbin/truthy-dpo-v0.1
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How to use BarraHome/zephyr-dpo-v2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="BarraHome/zephyr-dpo-v2") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BarraHome/zephyr-dpo-v2")
model = AutoModelForCausalLM.from_pretrained("BarraHome/zephyr-dpo-v2")How to use BarraHome/zephyr-dpo-v2 with Unsloth Studio:
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for BarraHome/zephyr-dpo-v2 to start chatting
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for BarraHome/zephyr-dpo-v2 to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for BarraHome/zephyr-dpo-v2 to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="BarraHome/zephyr-dpo-v2",
max_seq_length=2048,
)This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 59.99 |
| AI2 Reasoning Challenge (25-Shot) | 57.85 |
| HellaSwag (10-Shot) | 82.72 |
| MMLU (5-Shot) | 58.61 |
| TruthfulQA (0-shot) | 56.16 |
| Winogrande (5-shot) | 74.35 |
| GSM8k (5-shot) | 30.25 |