Quantization made by Richard Erkhov.

Evolutionstory-7B-v2.2 - GGUF

Model creator: https://huggingface.co/ClaudioItaly/
Original model: https://huggingface.co/ClaudioItaly/Evolutionstory-7B-v2.2/

Name	Quant method	Size
Evolutionstory-7B-v2.2.Q2_K.gguf	Q2_K	2.53GB
Evolutionstory-7B-v2.2.IQ3_XS.gguf	IQ3_XS	2.81GB
Evolutionstory-7B-v2.2.IQ3_S.gguf	IQ3_S	2.96GB
Evolutionstory-7B-v2.2.Q3_K_S.gguf	Q3_K_S	2.95GB
Evolutionstory-7B-v2.2.IQ3_M.gguf	IQ3_M	3.06GB
Evolutionstory-7B-v2.2.Q3_K.gguf	Q3_K	3.28GB
Evolutionstory-7B-v2.2.Q3_K_M.gguf	Q3_K_M	3.28GB
Evolutionstory-7B-v2.2.Q3_K_L.gguf	Q3_K_L	3.56GB
Evolutionstory-7B-v2.2.IQ4_XS.gguf	IQ4_XS	3.67GB
Evolutionstory-7B-v2.2.Q4_0.gguf	Q4_0	3.83GB
Evolutionstory-7B-v2.2.IQ4_NL.gguf	IQ4_NL	3.87GB
Evolutionstory-7B-v2.2.Q4_K_S.gguf	Q4_K_S	3.86GB
Evolutionstory-7B-v2.2.Q4_K.gguf	Q4_K	4.07GB
Evolutionstory-7B-v2.2.Q4_K_M.gguf	Q4_K_M	4.07GB
Evolutionstory-7B-v2.2.Q4_1.gguf	Q4_1	4.24GB
Evolutionstory-7B-v2.2.Q5_0.gguf	Q5_0	4.65GB
Evolutionstory-7B-v2.2.Q5_K_S.gguf	Q5_K_S	4.65GB
Evolutionstory-7B-v2.2.Q5_K.gguf	Q5_K	4.78GB
Evolutionstory-7B-v2.2.Q5_K_M.gguf	Q5_K_M	4.78GB
Evolutionstory-7B-v2.2.Q5_1.gguf	Q5_1	5.07GB
Evolutionstory-7B-v2.2.Q6_K.gguf	Q6_K	5.53GB
Evolutionstory-7B-v2.2.Q8_0.gguf	Q8_0	7.17GB

Original model description:

base_model: - BarraHome/Mistroll-7B-v2.2 - ClaudioItaly/Evolutionstory library_name: transformers tags: - mergekit - merge license: mit

merge

I finally think I managed to make a model with high writing skills. Based on the prompt it manages to be very coherent with the story.

It also has great RAG capabilities.

This is a merge of pre-trained language models created using mergekit.

The AI model “ClaudioItaly/Evolutionstory-7B-v2.2” has achieved interesting ratings in several metrics, but also shows some areas for improvement. Here is an analysis of the main findings and implications:

Strengths: IFEval (0-Shot): Scored a very solid 48.14 in strict accuracy. This indicates that the model handles text generation tasks well without the need for prior examples, demonstrating good immediate comprehension capabilities. BBH (3-Shot): The score of 31.62 in this 3-shot dataset (where the model receives a few examples before responding) suggests that the model is able to effectively leverage additional context to improve performance. Areas of Improvement: Math and Complex Reasoning (MATH Lvl 5, 4-Shot): A score of 6.42 on this advanced math level test highlights that the model struggles with complex logic or math problems, which is typical of many general language models, which do not they are optimized for solving numerical or structured problems.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: BarraHome/Mistroll-7B-v2.2
  - model: ClaudioItaly/Evolutionstory
merge_method: slerp
base_model: ClaudioItaly/Evolutionstory
dtype: bfloat16
parameters:
  t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers