Edit model card

image/jpeg

πŸ‘‘ NeuralMonarch-7B

NeuralMonarch-7B is a DPO fine-tuned of mlabonne/Monarch-7B using the jondurbin/truthy-dpo-v0.1 and argilla/distilabel-intel-orca-dpo-pairs preference datasets.

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

Special thanks to Jon Durbin, Intel, and Argilla for the preference datasets.

Try the demo: https://huggingface.co/spaces/mlabonne/NeuralMonarch-7B-GGUF-Chat

πŸ” Applications

This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio).

Compared to other 7B models, it performs well in instruction following and reasoning tasks. For a chat/RP model with strong reasoning abilities, check out mlabonne/AlphaMonarch-7B.

⚑ Quantized models

πŸ† Evaluation

Nous

NeuralMonarch-7B is one of the best-performing 7B models on Nous' benchmark suite (evaluation performed using LLM AutoEval). See the entire leaderboard here.

Model Average AGIEval GPT4All TruthfulQA Bigbench
NeuralMonarch-7B πŸ“„ 62.73 45.31 76.99 78.35 50.28
AlphaMonarch-7B πŸ“„ 62.74 45.37 77.01 78.39 50.2
Monarch-7B πŸ“„ 62.68 45.48 77.07 78.04 50.14
teknium/OpenHermes-2.5-Mistral-7B πŸ“„ 52.42 42.75 72.99 52.99 40.94
mlabonne/NeuralHermes-2.5-Mistral-7B πŸ“„ 53.51 43.67 73.24 55.37 41.76
mlabonne/NeuralBeagle14-7B πŸ“„ 60.25 46.06 76.77 70.32 47.86
mlabonne/NeuralOmniBeagle-7B πŸ“„ 62.3 45.85 77.26 76.06 50.03
eren23/dpo-binarized-NeuralTrix-7B πŸ“„ 62.5 44.57 76.34 79.81 49.27
CultriX/NeuralTrix-7B-dpo πŸ“„ 62.5 44.61 76.33 79.8 49.24

EQ-bench

NeuralMonarch-7B is also outperforming 70B and 120B parameter models on EQ-bench by Samuel J. Paech, who kindly ran the evaluations.

image/png

Open LLM Leaderboard

NeuralMonarch-7B is one of the best-performing 7B models on the Open LLM Leaderboard.

MT-Bench

########## First turn ##########
                                    score
model                       turn         
gpt-4                       1     8.95625
OmniBeagle-7B               1     8.31250
AlphaMonarch-7B             1     8.23750
claude-v1                   1     8.15000
NeuralMonarch-7B            1     8.09375
gpt-3.5-turbo               1     8.07500
claude-instant-v1           1     7.80000

########## Second turn ##########
                                     score
model                       turn          
gpt-4                       2     9.025000
claude-instant-v1           2     8.012658
OmniBeagle-7B               2     7.837500
gpt-3.5-turbo               2     7.812500
claude-v1                   2     7.650000
AlphaMonarch-7B             2     7.618750
NeuralMonarch-7B            2     7.375000

########## Average ##########
                                score
model                                
gpt-4                        8.990625
OmniBeagle-7B                8.075000
gpt-3.5-turbo                7.943750
AlphaMonarch-7B              7.928125
claude-instant-v1            7.905660
claude-v1                    7.900000
NeuralMonarch-7B             7.734375
NeuralBeagle14-7B            7.628125

πŸ’» Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/NeuralMonarch-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"])
Downloads last month
14,876
Safetensors
Model size
7.24B params
Tensor type
FP16
Β·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for mlabonne/NeuralMonarch-7B

Finetuned
(8)
this model
Adapters
7 models
Finetunes
20 models
Merges
6 models
Quantizations
11 models

Spaces using mlabonne/NeuralMonarch-7B 6

Collection including mlabonne/NeuralMonarch-7B

Evaluation results