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metadata
language:
  - en
library_name: transformers
pipeline_tag: text-generation
datasets:
  - jondurbin/airoboros-2.2
  - Open-Orca/OpenOrca
  - garage-bAInd/Open-Platypus
  - WizardLM/WizardLM_evol_instruct_V2_196k
  - TokenBender/python_eval_instruct_51k
tags:
  - code
license: apache-2.0
model-index:
  - name: SpeechlessCoder
    results:
      - task:
          type: text-generation
        dataset:
          type: openai_humaneval
          name: HumanEval
        metrics:
          - name: pass@1
            type: pass@1
            value: 51.21951219512195
            verified: false

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: “python” 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