metadata
library_name: transformers
license: apache-2.0
base_model: mistralai/Mistral-7B-vp0.3
language: en
datasets:
- Word2Li/MiddOptimized
tags:
- llama-factory
- full
pipeline_tag: text-generation
model-index:
- name: Mistral-7B-v0.3-Middo-Alpaca
results:
- task:
type: text-generation
dataset:
name: MMLU
type: MMLU
metrics:
- name: weighted accuracy
type: weighted accuracy
value: 38.73
verified: true
- task:
type: text-generation
dataset:
name: IFEval
type: IFEval
metrics:
- name: overall accuracy
type: overall accuracy
value: 44.01
verified: true
- task:
type: text-generation
dataset:
name: GSM8K
type: GSM8K
metrics:
- name: accuracy
type: accuracy
value: 34.8
verified: true
- task:
type: text-generation
dataset:
name: MATH
type: MATH
metrics:
- name: accuracy
type: accuracy
value: 6.64
verified: true
- task:
type: text-generation
dataset:
name: HumanEval
type: HumanEval
metrics:
- name: humaneval_pass@1
type: humaneval_pass@1
value: 26.22
verified: true
- task:
type: text-generation
dataset:
name: MBPP
type: MBPP
metrics:
- name: score
type: score
value: 31.4
verified: true
- task:
type: text-generation
dataset:
name: Hellaswag
type: Hellaswag
metrics:
- name: accuracy
type: accuracy
value: 44.86
verified: true
- task:
type: text-generation
dataset:
name: GPQA
type: GPQA
metrics:
- name: accuracy
type: accuracy
value: 11.11
verified: true
metrics:
- accuracy
Mistral-7B-v0.3-Middo-Alpaca
Code: https://github.com/Word2VecT/Middo
Model description
This model is a fine-tuned version of mistralai/Mistral-7B-v0.3 on the MiddOptimzed/llama_alpaca dataset.
Training and evaluation data
Training data
Middo optimized Alpaca on mistralai/Mistral-7B-v0.3.
Evaluation data
- General
- MMLU
- IFEval
- Math
- GSM8K
- MATH
- Code
- HumanEval
- MBPP
- Reasoning
- Hellaswag
- GPQA
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1.0
Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1+cu121
- Datasets 2.21.0
- Tokenizers 0.20.1