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---
language:
- en
license: apache-2.0
tags:
- code
- mathematics
datasets:
- ajibawa-2023/Code-290k-ShareGPT
- m-a-p/Code-Feedback
- microsoft/orca-math-word-problems-200k
- teknium/openhermes
model-index:
- name: Code-Mistral-7B
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: 64.59
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-Mistral-7B
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: 85.29
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-Mistral-7B
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: 65.0
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-Mistral-7B
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: 54.64
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-Mistral-7B
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: 82.24
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-Mistral-7B
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: 68.08
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Code-Mistral-7B
name: Open LLM Leaderboard
---
**Code-Mistral-7B**
This Model is trained on refined version of my dataset [Code-290k-ShareGPT](https://huggingface.co/datasets/ajibawa-2023/Code-290k-ShareGPT).
Besides this it is trained on following datasets:
[Code-Feedback](https://huggingface.co/datasets/m-a-p/Code-Feedback)
[orca-math-word-problems-200k](https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k)
[Openhermes](https://huggingface.co/datasets/teknium/openhermes)
The idea was to check how this Model will perform with both Code & Maths datasets. This model is very good with Coding.
Maths is still hit & miss but you can test out this model.
This Model is trained on massive datasets so the results are very good.
I have used ChatML prompt format.
Kindly note this is qLoRA version, a rare exception.
**GGUF & Exllama**
GGUF: [Link](https://huggingface.co/bartowski/Code-Mistral-7B-GGUF)
Exllama v2: [Link](https://huggingface.co/bartowski/Code-Mistral-7B-exl2)
Special Thanks to [Bartowski](https://huggingface.co/bartowski) for quantizing this model.
**Training:**
Entire dataset was trained on 4 x A100 80GB. For 3 epoch, training took almost 33 Hours. Axolotl codebase was used for training purpose.
Entire data is trained on Mistral.
**Example Prompt:**
This model uses **ChatML** prompt format.
```
<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
You can modify above Prompt as per your requirement.
I want to say special Thanks to the Open Source community for helping & guiding me to better understand the AI/Model development.
Thank you for your love & support.
**Example Output**
**C++**
**Error Resolving**
**Matrices**
**Machine Learning**
# [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_ajibawa-2023__Code-Mistral-7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |69.97|
|AI2 Reasoning Challenge (25-Shot)|64.59|
|HellaSwag (10-Shot) |85.29|
|MMLU (5-Shot) |65.00|
|TruthfulQA (0-shot) |54.64|
|Winogrande (5-shot) |82.24|
|GSM8k (5-shot) |68.08|
|