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## Nous Benchmark
| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|---------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[Nandine-7b](https://huggingface.co/sethuiyer/Nandine-7b)| 43.54| 76.41| 61.73| 45.27| 56.74|
### AGIEval
| Task |Version| Metric |Value| |Stderr|
|------------------------------|------:|--------|----:|---|-----:|
|agieval_aqua_rat | 0|acc |23.62|± | 2.67|
| | |acc_norm|22.05|± | 2.61|
|agieval_logiqa_en | 0|acc |37.94|± | 1.90|
| | |acc_norm|38.71|± | 1.91|
|agieval_lsat_ar | 0|acc |26.09|± | 2.90|
| | |acc_norm|22.61|± | 2.76|
|agieval_lsat_lr | 0|acc |47.45|± | 2.21|
| | |acc_norm|50.00|± | 2.22|
|agieval_lsat_rc | 0|acc |60.97|± | 2.98|
| | |acc_norm|59.85|± | 2.99|
|agieval_sat_en | 0|acc |77.18|± | 2.93|
| | |acc_norm|77.67|± | 2.91|
|agieval_sat_en_without_passage| 0|acc |45.63|± | 3.48|
| | |acc_norm|45.15|± | 3.48|
|agieval_sat_math | 0|acc |35.91|± | 3.24|
| | |acc_norm|32.27|± | 3.16|
Average: 43.54%
### GPT4All
| Task |Version| Metric |Value| |Stderr|
|-------------|------:|--------|----:|---|-----:|
|arc_challenge| 0|acc |63.74|± | 1.40|
| | |acc_norm|63.99|± | 1.40|
|arc_easy | 0|acc |85.94|± | 0.71|
| | |acc_norm|83.50|± | 0.76|
|boolq | 1|acc |87.80|± | 0.57|
|hellaswag | 0|acc |67.50|± | 0.47|
| | |acc_norm|85.31|± | 0.35|
|openbookqa | 0|acc |38.20|± | 2.18|
| | |acc_norm|49.40|± | 2.24|
|piqa | 0|acc |82.97|± | 0.88|
| | |acc_norm|84.33|± | 0.85|
|winogrande | 0|acc |80.51|± | 1.11|
Average: 76.41%
### TruthfulQA
| Task |Version|Metric|Value| |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc| 1|mc1 |45.78|± | 1.74|
| | |mc2 |61.73|± | 1.54|
Average: 61.73%
### Bigbench
| Task |Version| Metric |Value| |Stderr|
|------------------------------------------------|------:|---------------------|----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|57.89|± | 3.59|
|bigbench_date_understanding | 0|multiple_choice_grade|65.58|± | 2.48|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|38.76|± | 3.04|
|bigbench_geometric_shapes | 0|multiple_choice_grade|20.06|± | 2.12|
| | |exact_str_match | 5.85|± | 1.24|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|30.20|± | 2.06|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|20.71|± | 1.53|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|52.67|± | 2.89|
|bigbench_movie_recommendation | 0|multiple_choice_grade|43.60|± | 2.22|
|bigbench_navigate | 0|multiple_choice_grade|50.50|± | 1.58|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|73.15|± | 0.99|
|bigbench_ruin_names | 0|multiple_choice_grade|46.65|± | 2.36|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|25.25|± | 1.38|
|bigbench_snarks | 0|multiple_choice_grade|75.14|± | 3.22|
|bigbench_sports_understanding | 0|multiple_choice_grade|73.12|± | 1.41|
|bigbench_temporal_sequences | 0|multiple_choice_grade|47.20|± | 1.58|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|23.04|± | 1.19|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|18.69|± | 0.93|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|52.67|± | 2.89|
Average: 45.27%
Average score: 56.74%
Elapsed time: 01:47:54

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---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- merge
- mergekit
- lazymergekit
inference: false
base_model:
- senseable/Westlake-7B
- Guilherme34/Samantha-v2
- uukuguy/speechless-mistral-six-in-one-7b
pipeline_tag: text-generation
model-index:
- name: sethuiyer/Nandine-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: 69.28
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-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: 87.01
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-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: 64.83
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-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: 62.1
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-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: 83.19
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-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: 62.4
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sethuiyer/Nandine-7b
name: Open LLM Leaderboard
---
# Nandine-7b
<p align="center">
<img src="https://huggingface.co/sethuiyer/Nandine-7b/resolve/main/nandine.webp" height="128px" alt="Nandine">
</p>
This is Nandine-7b, rated **87.47/100** by GPT-4 on a collection of 30 synthetic prompts generated by GPT-4.
Nandine-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [senseable/Westlake-7B](https://huggingface.co/senseable/Westlake-7B)
* [Guilherme34/Samantha-v2](https://huggingface.co/Guilherme34/Samantha-v2)
* [uukuguy/speechless-mistral-six-in-one-7b](https://huggingface.co/uukuguy/speechless-mistral-six-in-one-7b)
Nandine-7b represents a harmonious amalgamation of narrative skill, empathetic interaction, intellectual depth, and eloquent communication.
## OpenLLM Benchmark
| Model | Average ⬆️ | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|--------------------------------|------------|-------|-----------|-------|------------|------------|-------|
| sethuiyer/Nandine-7b 📑 | 71.47 | 69.28 | 87.01 | 64.83 | 62.1 | 83.19 | 62.4 |
## Nous Benchmark
| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|---------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[Nandine-7b](https://huggingface.co/sethuiyer/Nandine-7b)| 43.54| 76.41| 61.73| 45.27| 56.74|
For more details, refer [here](https://huggingface.co/sethuiyer/Nandine-7b/blob/main/EVAL.md)
**Pros:**
1. **Strong Narrative Skills:** Excels in storytelling, creating engaging and imaginative narratives.
2. **Accurate Information Delivery:** Provides factual and detailed information across various topics.
3. **Comprehensive Analysis:** Capable of well-rounded discussions on complex and ethical topics.
4. **Emotional Intelligence:** Shows empathy and understanding in responses requiring emotional sensitivity.
5. **Clarity and Structure:** Maintains clear and well-structured communication.
**Cons:**
1. **Language Translation Limitations:** Challenges in providing fluent and natural translations.
2. **Incomplete Problem Solving:** Some logical or mathematical problems are not solved accurately.
3. **Lack of Depth in Certain Areas:** Needs deeper exploration in some responses for a more comprehensive understanding.
4. **Occasional Imbalance in Historical Context:** Some historical explanations could be more balanced.
5. **Room for Enhanced Creativity:** While creative storytelling is strong, there's potential for more varied responses in hypothetical scenarios.
**Intended Use:**
Ideal for users seeking a versatile AI companion for creative writing, thoughtful discussions, and general assistance.
## 🧩 Configuration
```yaml
models:
- model: senseable/Westlake-7B
parameters:
weight: 0.55
density: 0.6
- model: Guilherme34/Samantha-v2
parameters:
weight: 0.10
density: 0.3
- model: uukuguy/speechless-mistral-six-in-one-7b
parameters:
weight: 0.35
density: 0.6
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "sethuiyer/Nandine-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"])
```
## GGUF
GGUF files are available at [Nandine-7b-GGUF](https://huggingface.co/sethuiyer/Nandine-7b-GGUF/tree/main)
## Ollama
Nandine is now available on Ollama. You can use it by running the command ```ollama run stuehieyr/nandine``` in your
terminal. If you have limited computing resources, check out this [video](https://www.youtube.com/watch?v=Qa1h7ygwQq8) to learn how to run it on
a Google Colab backend.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_sethuiyer__Nandine-7b)
| Metric |Value|
|---------------------------------|----:|
|Avg. |71.47|
|AI2 Reasoning Challenge (25-Shot)|69.28|
|HellaSwag (10-Shot) |87.01|
|MMLU (5-Shot) |64.83|
|TruthfulQA (0-shot) |62.10|
|Winogrande (5-shot) |83.19|
|GSM8k (5-shot) |62.40|

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{
"_name_or_path": "mistralai/Mistral-7B-v0.1",
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-05,
"rope_theta": 10000.0,
"sliding_window": 4096,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.37.1",
"use_cache": true,
"vocab_size": 32000
}

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models:
- model: senseable/Westlake-7B
parameters:
weight: 0.55
density: 0.6
- model: Guilherme34/Samantha-v2
parameters:
weight: 0.10
density: 0.3
- model: uukuguy/speechless-mistral-six-in-one-7b
parameters:
weight: 0.35
density: 0.6
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
dtype: bfloat16

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