speechless-code-mistral-orca-7b-v1.0

Use the following dataset to fine-tune Open-Orca/Mistral-7B-OpenOrca in order to improve the model's reasoning and planning abilities.

Total 201,981 samples.

• jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples.
• Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples.
• garage-bAInd/Open-Platypus: 100%, 24,926 samples.
• WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,185 samples
• TokenBender/python_eval_instruct_51k: “python” in output .40,309 samples
• Spider: 8,659 samples

Code: https://github.com/uukuguy/speechless

HumanEval

Metric	Value
humaneval-python	47.561

Big Code Models Leaderboard

CodeLlama-34B-Python: 53.29

CodeLlama-34B-Instruct: 50.79

CodeLlama-13B-Instruct: 50.6

CodeLlama-34B: 45.11

CodeLlama-13B-Python: 42.89

CodeLlama-13B: 35.07

lm-evaluation-harness

Open LLM Leaderboard

Metric	Value
ARC	59.64
HellaSwag	82.25
MMLU	61.33
TruthfulQA	48.45
Average	62.92

Parameters

lr	2e-4
lr_scheduler_type	cosine
weight_decay	0.0
optim	paged_adamw_8bit
flash_attention	True
rerope	False
max_new_tokens	4096
num_train_epochs	2
bits	4
lora_r	64
lora_alpha	16
lora_dropout	0.05
double_quant	True
quant_type	nf4
dataset_format	airoboros
mini_batch_size	2
grandient_accumulation_steps	32
bf16	True

A100-40G x 4

epoch	2.0
etrain_loss	0.4708
etrain_runtime	12:12:53.64
etrain_samples_per_second	9.002
etrain_steps_per_second	0.07
eeval_loss	0.4851
eeval_runtime	0:00:10.31
eeval_samples_per_second	19.385
eeval_steps_per_second	4.846

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	55.33
ARC (25-shot)	59.64
HellaSwag (10-shot)	82.25
MMLU (5-shot)	61.33
TruthfulQA (0-shot)	48.45
Winogrande (5-shot)	77.51
GSM8K (5-shot)	8.26
DROP (3-shot)	49.89