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: 54.27
verified: false
speechless-coding-7b-16k-tora
Use the following dataset to fine-tune llm_agents/tora-code-7b-v0.1 in order to improve the model's reasoning and planning abilities.
prompt_type = "alpaca" max_tokens > 128 && < 16384
Total 177,333 samples 316 MB
- jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 21,923 samples.
- Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 62,973 samples.
- garage-bAInd/Open-Platypus: 100%, 22,760 samples.
- WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,081 samples
- TokenBender/python_eval_instruct_51k: “python” in output .39,596 samples
HumanEval
Metric | Value |
---|---|
humaneval-python | 54.27 |
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
MultiPL-E
Metric | Value |
---|---|
python | 59.63 |
java | 32.28 |
javascript | 46.58 |
cpp | 37.83 |
rust | 28.21 |
go | 27.27 |
sh | 13.29 |
julia | 34.59 |
typescript | 47.80 |
LMEval
Metric | Value |
---|---|
ARC | |
HellaSwag | |
MMLU | |
TruthfulQA | |
Average |
Parameters
lr | 2e-4 |
lr_scheduler_type | cosine |
weight_decay | 0.0 |
optim | paged_adamw_8bit |
flash_attention | True |
rerope | False |
max_new_tokens | 16384 |
num_train_epochs | 2 |
bits | 4 |
lora_r | 64 |
lora_alpha | 256 |
lora_dropout | 0.05 |
double_quant | True |
quant_type | nf4 |
dataset_format | sharegpt |
mini_batch_size | 2 |
grandient_accumulation_steps | 32 |
bf16 | True |
A100-40G x 4