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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: 51.829
            verified: false

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

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

Use the following dataset to fine-tune llm_agents/tora-code-7b-v1.0 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 51.829

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 42.66
HellaSwag 65.16
MMLU 38.56
TruthfulQA 42.06
Average 47.11

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

A800-80G x 2

epoch 2.0
etrain_loss 0.5891
etrain_runtime 19:24:49.43
etrain_samples_per_second 5.664
etrain_steps_per_second 0.044
eeval_loss 0.5872
eeval_runtime 0:00:15.59
eeval_samples_per_second 12.822
eeval_steps_per_second 6.411

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 40.1
ARC (25-shot) 42.66
HellaSwag (10-shot) 65.16
MMLU (5-shot) 38.56
TruthfulQA (0-shot) 42.06
Winogrande (5-shot) 62.9
GSM8K (5-shot) 0.91
DROP (3-shot) 28.48