---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- openchat/openchat-3.5-0106
- teknium/OpenHermes-2.5-Mistral-7B
base_model:
- openchat/openchat-3.5-0106
- teknium/OpenHermes-2.5-Mistral-7B
model-index:
- name: chatty-djinn-14B
  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: 70.39
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mayacinka/chatty-djinn-14B
      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: 86.45
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mayacinka/chatty-djinn-14B
      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: 64.4
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mayacinka/chatty-djinn-14B
      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: 67.57
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mayacinka/chatty-djinn-14B
      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: 83.11
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mayacinka/chatty-djinn-14B
      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: 60.58
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mayacinka/chatty-djinn-14B
      name: Open LLM Leaderboard
---
![thumbnail](djinn-14b.webp)
# djinn


djinn is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106)
* [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)
* bardsai/jaskier-7b-dpo-v6.1
* senseable/WestLake-7B-v2
* NousResearch/Nous-Hermes-2-Mistral-7B-DPO
* paulml/OGNO-7B
* paulml/DPOB-INMTOB-7B
* mlabonne/AlphaMonarch-7B

# 🏆 Benchmarks
Nous benchmarks, find more [details here](https://gist.github.com/majacinka/3f2a797c8872ca9bfdaa2bbf3369edb5)

|                                Model                                |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|---------------------------------------------------------------------|------:|------:|---------:|-------:|------:|
|[chatty-djinn-14B](https://huggingface.co/mayacinka/chatty-djinn-14B)|  38.43|  76.29|     68.02|    47.6|  57.59|

### AGIEval
|             Task             |Version| Metric |Value|   |Stderr|
|------------------------------|------:|--------|----:|---|-----:|
|agieval_aqua_rat              |      0|acc     |23.62|±  |  2.67|
|                              |       |acc_norm|21.65|±  |  2.59|
|agieval_logiqa_en             |      0|acc     |32.26|±  |  1.83|
|                              |       |acc_norm|33.79|±  |  1.86|
|agieval_lsat_ar               |      0|acc     |23.04|±  |  2.78|
|                              |       |acc_norm|23.04|±  |  2.78|
|agieval_lsat_lr               |      0|acc     |38.82|±  |  2.16|
|                              |       |acc_norm|39.22|±  |  2.16|
|agieval_lsat_rc               |      0|acc     |59.48|±  |  3.00|
|                              |       |acc_norm|54.65|±  |  3.04|
|agieval_sat_en                |      0|acc     |75.73|±  |  2.99|
|                              |       |acc_norm|74.27|±  |  3.05|
|agieval_sat_en_without_passage|      0|acc     |35.92|±  |  3.35|
|                              |       |acc_norm|34.47|±  |  3.32|
|agieval_sat_math              |      0|acc     |31.36|±  |  3.14|
|                              |       |acc_norm|26.36|±  |  2.98|

Average: 38.43%

### GPT4All
|    Task     |Version| Metric |Value|   |Stderr|
|-------------|------:|--------|----:|---|-----:|
|arc_challenge|      0|acc     |62.12|±  |  1.42|
|             |       |acc_norm|65.44|±  |  1.39|
|arc_easy     |      0|acc     |83.88|±  |  0.75|
|             |       |acc_norm|78.58|±  |  0.84|
|boolq        |      1|acc     |88.07|±  |  0.57|
|hellaswag    |      0|acc     |65.18|±  |  0.48|
|             |       |acc_norm|86.45|±  |  0.34|
|openbookqa   |      0|acc     |39.60|±  |  2.19|
|             |       |acc_norm|48.60|±  |  2.24|
|piqa         |      0|acc     |82.26|±  |  0.89|
|             |       |acc_norm|83.62|±  |  0.86|
|winogrande   |      0|acc     |83.27|±  |  1.05|

Average: 76.29%

### TruthfulQA
|    Task     |Version|Metric|Value|   |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc|      1|mc1   |50.55|±  |  1.75|
|             |       |mc2   |68.02|±  |  1.52|

Average: 68.02%

### Bigbench
|                      Task                      |Version|       Metric        |Value|   |Stderr|
|------------------------------------------------|------:|---------------------|----:|---|-----:|
|bigbench_causal_judgement                       |      0|multiple_choice_grade|57.89|±  |  3.59|
|bigbench_date_understanding                     |      0|multiple_choice_grade|64.50|±  |  2.49|
|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|32.56|±  |  2.92|
|bigbench_geometric_shapes                       |      0|multiple_choice_grade|26.18|±  |  2.32|
|                                                |       |exact_str_match      | 1.11|±  |  0.55|
|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|30.80|±  |  2.07|
|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|22.86|±  |  1.59|
|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|57.67|±  |  2.86|
|bigbench_movie_recommendation                   |      0|multiple_choice_grade|62.00|±  |  2.17|
|bigbench_navigate                               |      0|multiple_choice_grade|56.20|±  |  1.57|
|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|65.65|±  |  1.06|
|bigbench_ruin_names                             |      0|multiple_choice_grade|64.73|±  |  2.26|
|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|17.33|±  |  1.20|
|bigbench_snarks                                 |      0|multiple_choice_grade|76.24|±  |  3.17|
|bigbench_sports_understanding                   |      0|multiple_choice_grade|75.15|±  |  1.38|
|bigbench_temporal_sequences                     |      0|multiple_choice_grade|48.90|±  |  1.58|
|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|22.32|±  |  1.18|
|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|18.17|±  |  0.92|
|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|57.67|±  |  2.86|

Average: 47.6%

Average score: 57.59%
## 🧩 Configuration
Inspired by [theprofessor's config](https://huggingface.co/abacusai/TheProfessor-155b)

```yaml
merge_method: linear # use linear so we can include multiple models, albeit at a zero weight
parameters:
  weight: 1.0 # weight everything as 1 unless specified otherwise - linear with one model weighted at 1 is a no-op like passthrough
slices:
  - sources:
      - model: openchat/openchat-3.5-0106
        layer_range: [0, 1]
      - model: teknium/OpenHermes-2.5-Mistral-7B 
        layer_range: [0, 1]
        parameters:
          weight: 0
  - sources:
      - model: bardsai/jaskier-7b-dpo-v6.1
        layer_range: [1, 10]
  - sources:
      - model: senseable/WestLake-7B-v2
        layer_range: [10, 20]
  - sources:
      - model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
        layer_range: [20, 30]
  - sources:
      - model: paulml/OGNO-7B
        layer_range: [15, 25]
  - sources:
      - model: paulml/DPOB-INMTOB-7B
        layer_range: [22, 32]
  - sources:
      - model: mlabonne/AlphaMonarch-7B
        layer_range: [5, 15]
  - sources: 
      - model: openchat/openchat-3.5-0106
        layer_range: [31, 32]
      - model: teknium/OpenHermes-2.5-Mistral-7B
        layer_range: [31, 32]
        parameters:
          weight: 0
dtype: float16
tokenizer_source: model:openchat/openchat-3.5-0106
```

## An example

*User:* Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have? 
Think step by step and the result MUST BE A NUMBER

*djinn:*
In this family structure, we know there are 4 aka '1 + 3-brothers' (1 being initially for Sally) existing individuals who are 
also the siblings of the 3-brother-group. As each of the 3-brothers has 2-sisters each, that sums up to 6 total sister- roles. Since 
we already accounted for one of the sisters (assuming to be the oldest or in alphabetical order, as names were not given) to be part 
of the 3-brother group, we subtract one from the 6-sisters total. So, considering the provided information, Sally would have 6 - 1 = 5 
remaining younger or alphabetically-wise- placed, biological-sisters. However, note that this problem statement might not perfectly align 
with real-life complexities, such as same-sex twins, adoption, etc., but within the given context, the solution is 5.

Remember, in more intricate or unclear family tree scenarios, it'd be best to directly ask for clarifications or consider additional data 
points. In short, with the given constraints, Sally has 5 known, direct, biological, and likely-in-age-order, younger/alphabetically-placed,
sister-siblings.(To reiterate the core result in a concise manner: 5).

(Please excuse the verbosity, aiming for thoroughness and clarity.)

This response maintains the initial request's numerical focus while providing a detailed explanation to ensure understanding.

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mayacinka/djinn"
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"])
```
# [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_mayacinka__chatty-djinn-14B)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |72.08|
|AI2 Reasoning Challenge (25-Shot)|70.39|
|HellaSwag (10-Shot)              |86.45|
|MMLU (5-Shot)                    |64.40|
|TruthfulQA (0-shot)              |67.57|
|Winogrande (5-shot)              |83.11|
|GSM8k (5-shot)                   |60.58|