speechless-coding-7b-16k-tora

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.

context window length: 16,384 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

50 samples/T=0.2/MaxTokens=512/Top_P=0.95

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:

Metric	Value
humaneval-python	52.44

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


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

Quantizations