license, tags, base_model, model-index

license	tags	base_model	model-index
cc-by-nc-4.0	merge mergekit lazymergekit dpo rlhf	mlabonne/Beagle14-7B	name results NeuralBeagle14-7B task dataset metrics source type name text-generation Text Generation name type config split args AI2 Reasoning Challenge (25-Shot) ai2_arc ARC-Challenge test num_few_shot 25 type value name acc_norm 72.95 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralBeagle14-7B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type split args HellaSwag (10-Shot) hellaswag validation num_few_shot 10 type value name acc_norm 88.34 normalized accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralBeagle14-7B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args MMLU (5-Shot) cais/mmlu all test num_few_shot 5 type value name acc 64.55 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralBeagle14-7B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args TruthfulQA (0-shot) truthful_qa multiple_choice validation num_few_shot 0 type value mc2 69.93 url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralBeagle14-7B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args Winogrande (5-shot) winogrande winogrande_xl validation num_few_shot 5 type value name acc 82.4 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralBeagle14-7B Open LLM Leaderboard task dataset metrics source type name text-generation Text Generation name type config split args GSM8k (5-shot) gsm8k main test num_few_shot 5 type value name acc 70.28 accuracy url name https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/NeuralBeagle14-7B Open LLM Leaderboard

🐶 NeuralBeagle14-7B

Update 01/16/24: NeuralBeagle14-7B is (probably) the best 7B model you can find! 🎉

NeuralBeagle14-7B is a DPO fine-tune of mlabonne/Beagle14-7B using the argilla/distilabel-intel-orca-dpo-pairs preference dataset and my DPO notebook from this article.

It is based on a merge of the following models using LazyMergekit:

• fblgit/UNA-TheBeagle-7b-v1, based on jondurbin's repo and jondurbin/bagel-v0.3
• argilla/distilabeled-Marcoro14-7B-slerp, based on mlabonne/Marcoro14-7B-slerp

Thanks Argilla for providing the dataset and the training recipe here. 💪

You can try it out in this Space (GGUF Q4_K_M).

🔍 Applications

This model uses a context window of 8k. It is compatible with different templates, like chatml and Llama's chat template.

Compared to other 7B models, it displays good performance in instruction following and reasoning tasks. It can also be used for RP and storytelling.

⚡ Quantized models

• GGUF: https://huggingface.co/mlabonne/NeuralBeagle14-7B-GGUF
• GPTQ: https://huggingface.co/TheBloke/NeuralBeagle14-7B-GPTQ
• AWQ: https://huggingface.co/TheBloke/NeuralBeagle14-7B-AWQ
• EXL2: https://huggingface.co/LoneStriker/NeuralBeagle14-7B-8.0bpw-h8-exl2

🏆 Evaluation

Open LLM Leaderboard

NeuralBeagle14-7B ranks first on the Open LLM Leaderboard in the ~7B category.

It has the same average score as Beagle14-7B ("Show merges"), which could be due to might be due to an unlucky run. I think I might be overexploiting argilla/distilabel-intel-orca-dpo-pairs at this point, since this dataset or its original version are present in multiple models. I need to find more high-quality preference data for the next DPO merge.

Note that some models like udkai/Turdus and nfaheem/Marcoroni-7b-DPO-Merge are unfortunately contaminated on purpose (see the very high Winogrande score).

Nous

The evaluation was performed using LLM AutoEval on Nous suite. It is the best 7B model to date.

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
mlabonne/NeuralBeagle14-7B 📄	60.25	46.06	76.77	70.32	47.86
mlabonne/Beagle14-7B 📄	59.4	44.38	76.53	69.44	47.25
mlabonne/NeuralDaredevil-7B 📄	59.39	45.23	76.2	67.61	48.52
argilla/distilabeled-Marcoro14-7B-slerp 📄	58.93	45.38	76.48	65.68	48.18
mlabonne/NeuralMarcoro14-7B 📄	58.4	44.59	76.17	65.94	46.9
openchat/openchat-3.5-0106 📄	53.71	44.17	73.72	52.53	44.4
teknium/OpenHermes-2.5-Mistral-7B 📄	52.42	42.75	72.99	52.99	40.94

You can find the complete benchmark on YALL - Yet Another LLM Leaderboard.

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/NeuralBeagle14-7B"
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"])