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
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.
Besides this it is trained on following datasets:
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
Exllama v2: Link
Special Thanks to 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
Detailed results can be found here
| 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 |