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Adding Evaluation Results (#1)
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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:
  - llama-2
  - code
license: llama2
model-index:
  - name: SpeechlessCoder
    results:
      - task:
          type: text-generation
        dataset:
          type: openai_humaneval
          name: HumanEval
        metrics:
          - name: pass@1
            type: pass@1
            value: 47.561
            verified: false

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