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RichardErkhov/
mlabonne_-_NeuralBeagle14-7B-gguf

Name: mlabonne_-_NeuralBeagle14-7B-gguf
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NeuralBeagle14-7B - GGUF

Model creator: https://huggingface.co/mlabonne/
Original model: https://huggingface.co/mlabonne/NeuralBeagle14-7B/

Name	Quant method	Size
NeuralBeagle14-7B.Q2_K.gguf	Q2_K	2.53GB
NeuralBeagle14-7B.IQ3_XS.gguf	IQ3_XS	2.81GB
NeuralBeagle14-7B.IQ3_S.gguf	IQ3_S	2.96GB
NeuralBeagle14-7B.Q3_K_S.gguf	Q3_K_S	2.95GB
NeuralBeagle14-7B.IQ3_M.gguf	IQ3_M	3.06GB
NeuralBeagle14-7B.Q3_K.gguf	Q3_K	3.28GB
NeuralBeagle14-7B.Q3_K_M.gguf	Q3_K_M	3.28GB
NeuralBeagle14-7B.Q3_K_L.gguf	Q3_K_L	3.56GB
NeuralBeagle14-7B.IQ4_XS.gguf	IQ4_XS	3.67GB
NeuralBeagle14-7B.Q4_0.gguf	Q4_0	3.83GB
NeuralBeagle14-7B.IQ4_NL.gguf	IQ4_NL	3.87GB
NeuralBeagle14-7B.Q4_K_S.gguf	Q4_K_S	3.86GB
NeuralBeagle14-7B.Q4_K.gguf	Q4_K	4.07GB
NeuralBeagle14-7B.Q4_K_M.gguf	Q4_K_M	4.07GB
NeuralBeagle14-7B.Q4_1.gguf	Q4_1	4.24GB
NeuralBeagle14-7B.Q5_0.gguf	Q5_0	4.65GB
NeuralBeagle14-7B.Q5_K_S.gguf	Q5_K_S	4.65GB
NeuralBeagle14-7B.Q5_K.gguf	Q5_K	4.78GB
NeuralBeagle14-7B.Q5_K_M.gguf	Q5_K_M	4.78GB
NeuralBeagle14-7B.Q5_1.gguf	Q5_1	5.07GB
NeuralBeagle14-7B.Q6_K.gguf	Q6_K	5.53GB
NeuralBeagle14-7B.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: cc-by-nc-4.0 tags:

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

python

!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"])

RichardErkhov/mlabonne_-_NeuralBeagle14-7B-gguf