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 |