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chatty-djinn-14B/README.md
ModelHub XC f72c8d752f 初始化项目，由ModelHub XC社区提供模型 
Model: mayacinka/chatty-djinn-14B
Source: Original Platform
2026-04-11 15:44:05 +08:00

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---
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|
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