CodeCalc-Mistral-7B

Configuration

The following YAML configuration was used to produce this model:

base_model: uukuguy/speechless-code-mistral-7b-v1.0
dtype: bfloat16
merge_method: ties
models:
- model: uukuguy/speechless-code-mistral-7b-v1.0
- model: upaya07/Arithmo2-Mistral-7B
  parameters:
    density:  [0.25, 0.35, 0.45, 0.35, 0.25]
    weight: [0.1, 0.25, 0.5, 0.25, 0.1]
parameters:
  int8_mask: true

Evaluation

T	Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
🔍	sethuiyer/CodeCalc-Mistral-7B	66.33	61.95	83.64	62.78	47.79	78.3	63.53
📉	uukuguy/speechless-code-mistral-7b-v1.0	63.6	61.18	83.77	63.4	47.9	78.37	47.01

The merge appears to be successful, especially considering the substantial improvement in the GSM8K benchmark while maintaining comparable performance on other metrics.

Usage

Alpaca Instruction Format and Divine Intellect preset.

You are an intelligent programming assistant.

### Instruction:
Implement a linked list in C++

### Response:

Preset:

temperature: 1.31
top_p: 0.14
repetition_penalty: 1.17
top_k: 49

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	66.33
AI2 Reasoning Challenge (25-Shot)	61.95
HellaSwag (10-Shot)	83.64
MMLU (5-Shot)	62.78
TruthfulQA (0-shot)	47.79
Winogrande (5-shot)	78.30
GSM8k (5-shot)	63.53