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

• AWQ model(s) for GPU inference.
• GPTQ models for GPU inference, with multiple quantisation parameter options.
• 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference

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