Edit model card

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

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

Use the following dataset to fine-tune mistralai/Mistral-7B-v0.1 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: β€œpython” in output .40,309 samples
  • Spider: 8,659 samples

How to Prompt the Model

This model accepts the Alpaca instruction format.

For example:

You are an intelligent programming assistant.

### Instruction:
Implement a linked list in C++

### Response:

HumanEval

Metric Value
humaneval-python 51.21951219512195

Big Code Evaluation

Humaneval Java Javascript CPP Php Rust Swift R Lua D Racket Julia
pass@1 0.4260 0.3165 0.4241 0.3467 0.3548 0.2454 0.0000 0.1735 0.2942 0.1087 0.0000 0.3081
pass@10 0.5784 0.4506 0.5891 0.4845 0.4997 0.3858 0.0000 0.2516 0.4126 0.2018 0.0000 0.4427

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

{'ARC (acc_norm)': 0.6109215017064846,
'HellaSwag (acc_norm)': 0.8358892650866361,
'MMLU (acc)': 0.6325456394049195,
'TruthfulQA (mc2)': 0.4746745250371087,
'Winoground (acc)': 0.7829518547750592,
'GSM8K (acc)': 0.467778620166793,
'DROP (f1)': 0.49585675335570545,
'Open LLM Score': 0.61437428571428571}

Open LLM Leaderboard

Metric Value
ARC 60.58
HellaSwag 83.47
MMLU 62.98
TruthfulQA 47.9
Winoground 78.69
GSM8K 19.18
Average 58.85

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

A40-48G x 2

epoch 2.0
etrain_loss 0.5
etrain_runtime 1 day, 10:25:26.77
etrain_samples_per_second 3.194
etrain_steps_per_second 0.025
eeval_loss 0.5146
eeval_runtime 0:00:25.04
eeval_samples_per_second 7.985
eeval_steps_per_second

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 53.47
ARC (25-shot) 60.58
HellaSwag (10-shot) 83.75
MMLU (5-shot) 62.98
TruthfulQA (0-shot) 47.9
Winogrande (5-shot) 78.69
GSM8K (5-shot) 19.18
DROP (3-shot) 21.19
Downloads last month
4,903
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for uukuguy/speechless-code-mistral-7b-v1.0

Merges
2 models
Quantizations
3 models

Datasets used to train uukuguy/speechless-code-mistral-7b-v1.0

Spaces using uukuguy/speechless-code-mistral-7b-v1.0 7

Collection including uukuguy/speechless-code-mistral-7b-v1.0

Evaluation results