This finetune has seen several hundreds of millions of tokens of instruction and roleplaying data. A Kahneman-Tversky Optimization was applied as a Low Rank Adapter to give this model a unique output style.

Developed by Aura Industries, with contributions from Anthracite Org

Model Details

Model Name: Aura-8B
Base Model: arcee-ai/Llama-3.1-SuperNova-Lite
Model Type: Chat Completions
Prompt Format: Llama 3
License: Apache-2.0
Language: English
Max Context: 8,192+ tokens

License

This model is licensed under the Apache 2.0 License.

Quantizations

Static GGUF

Imatrix GGUF

EXL2

Open LLM Leaderboard Evaluation Results

Metric	Value
Avg.	27.34
IFEval (0-Shot)	72.05
BBH (3-Shot)	30.98
MATH Lvl 5 (4-Shot)	15.03
GPQA (0-shot)	4.81
MuSR (0-shot)	9.22
MMLU-PRO (5-shot)	31.93

Training Configuration

Click here for Axolotl configs

SFT

yaml

base_model: arcee-ai/Llama-3.1-SuperNova-Lite
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: FourOhFour/RP_Phase
    type: chat_template
    chat_template: llama3
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
  - path: Nitral-AI/Cybersecurity-ShareGPT
    type: chat_template
    chat_template: llama3
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
  - path: Nitral-AI/Medical_Instruct-ShareGPT
    type: chat_template
    chat_template: llama3
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
  - path: Nitral-AI/Olympiad_Math-ShareGPT
    type: chat_template
    chat_template: llama3
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
  - path: NewEden/Claude-Instruct-5k
    type: chat_template
    chat_template: llama3
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
  - path: lodrick-the-lafted/kalo-opus-instruct-3k-filtered
    type: chat_template
    chat_template: llama3
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
  - path: Nitral-AI/Creative_Writing-ShareGPT
    type: chat_template
    chat_template: llama3
    roles_to_train: ["gpt"]
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    train_on_eos: turn
  - path: jeiku/Writing
    type: completion
    field: text

shuffle_merged_datasets: true
dataset_prepared_path:
val_set_size: 0.01
output_dir: ./output/out

hub_model_id: jeiku/Aura-8B
hub_strategy: "all_checkpoints"
push_dataset_to_hub:
hf_use_auth_token: true

sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len:

wandb_project: Aura-8B
wandb_entity:
wandb_watch:
wandb_name: Aura-8B
wandb_log_model:

gradient_accumulation_steps: 16
micro_batch_size: 2
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 1e-5

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_ratio: 0.1
evals_per_epoch: 2
eval_table_size:
eval_max_new_tokens:
saves_per_epoch: 1
debug:
deepspeed: 
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|finetune_right_pad_id|>
  eos_token: <|eot_id|>

KTO

yaml

base_model: jeiku/Aura-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

hub_model_id: jeiku/aurakto
hub_strategy: "all_checkpoints"
push_dataset_to_hub:
hf_use_auth_token: true

chat_template: llama3

rl: kto
rl_beta: 0.2
kto_desirable_weight: 0.2

datasets:
  - path: anthracite-core/full-opus-chosen-hermes-rejected-kto-v1
    type: llama3.argilla

shuffle_merged_datasets: true
val_set_size: 0.0
output_dir: ./outputs/out

adapter: lora
lora_model_dir:

lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

sequence_len: 8192
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: false

wandb_project: Aura-8B
wandb_entity:
wandb_watch:
wandb_name: Aura-8B
wandb_log_model:

gradient_accumulation_steps: 16
micro_batch_size: 2
num_epochs: 2
max_steps: 500

optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0001
weight_decay: 0.05

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true
remove_unused_columns: false
early_stopping_patience:
resume_from_checkpoint: 
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
eval_max_new_tokens: 
saves_per_epoch: 1

debug:
deepspeed: 
fsdp:
fsdp_config:
fsdp:
fsdp_config:

special_tokens:
  pad_token: <|finetune_right_pad_id|>
  eos_token: <|eot_id|>

NewEden-Forge/Aura-8B-EXL2

These are EXL2 quants for Aura-8B, Measurement file in the main branch, Check revisions for different BPW

Aura-8B

Introduction

Model Details

License

Quantizations

Open LLM Leaderboard Evaluation Results

Training Configuration

NewEden-Forge/
Aura-8B-EXL2