NeuralDaredevil-7B
NeuralDaredevil-7B is a DPO fine-tune of mlabonne/Daredevil-7B using the argilla/distilabel-intel-orca-dpo-pairs preference dataset and my DPO notebook from this article.
Thanks Argilla for providing the dataset and the training recipe here. πͺ
π Evaluation
Nous
The evaluation was performed using LLM AutoEval on Nous suite.
Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
---|---|---|---|---|---|
mlabonne/NeuralDaredevil-7B π | 59.39 | 45.23 | 76.2 | 67.61 | 48.52 |
mlabonne/Beagle14-7B π | 59.4 | 44.38 | 76.53 | 69.44 | 47.25 |
argilla/distilabeled-Marcoro14-7B-slerp π | 58.93 | 45.38 | 76.48 | 65.68 | 48.18 |
mlabonne/NeuralMarcoro14-7B π | 58.4 | 44.59 | 76.17 | 65.94 | 46.9 |
openchat/openchat-3.5-0106 π | 53.71 | 44.17 | 73.72 | 52.53 | 44.4 |
teknium/OpenHermes-2.5-Mistral-7B π | 52.42 | 42.75 | 72.99 | 52.99 | 40.94 |
You can find the complete benchmark on YALL - Yet Another LLM Leaderboard.
Open LLM Leaderboard
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 74.12 |
AI2 Reasoning Challenge (25-Shot) | 69.88 |
HellaSwag (10-Shot) | 87.62 |
MMLU (5-Shot) | 65.12 |
TruthfulQA (0-shot) | 66.85 |
Winogrande (5-shot) | 82.08 |
GSM8k (5-shot) | 73.16 |
π» Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/NeuralDaredevil-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
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"])
Prompt Tempalte
This model uses the same prompt template as mistralai/Mistral-7B-Instruct-v0.2
See instruction-format for more details
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Base model
mlabonne/Daredevil-7BEvaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard69.880
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard87.620
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard65.120
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard66.850
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard82.080
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard73.160