Lambent/Eidolon-v2-14B

This version has been tuned from the fascinating arcee-ai/SuperNova-Medius as root model.

Censorship remains notable on this one, just including the Not For All Audiences tag due to dataset.

EQ-Bench is about 1 point lower than its ancestor, but fixed a syntax issue. May indicate a bit of expected intelligence loss.

Methodology: A bit of custom fine-tuning, with the plurality from the 'filtered' subset of argilla/ifeval-like-data experimentally trained with 'input/output' roles rather than 'user/assistant' (other instruction sampling stayed chatml-style, some continued pretraining added with a bias to older public domain styles); ties merged at full saturation with the original over base Qwen, then this DPO.

See axolotl config

axolotl version: 0.4.1

base_model: Lambent/proto-nova-eidolon-v2alpha0.3-14B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true

save_safetensors: true

load_in_8bit: false
load_in_4bit: true
strict: false

rl: dpo
# total_num_tokens: 
datasets:
  - path: Lambent/ai-deconditioning-synthesized-dpo
    split: train
    type: chatml.prompt_pairs
  - path: jondurbin/gutenberg-dpo-v0.1
    split: train
    type: chatml.prompt_pairs
  - path: nbeerbower/gutenberg2-dpo
    split: train
    type: chatml.prompt_pairs
  - path: unalignment/toxic-dpo-v0.2
    split: train
    type: chatml.prompt_pairs
  - path: vicgalle/configurable-system-prompt-multitask
    split: train
    type: chatml.prompt_pairs

dataset_prepared_path: prepared-dpo
output_dir: ./dpoq
val_set_size: 0.01

seed: 1

sequence_len: 2048
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: false

adapter: qlora
lora_model_dir:
lora_r: 256
lora_alpha: 256
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
peft_use_dora: true

wandb_project: eidolon-qwen2.5-qlora-dpo
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 16
micro_batch_size: 2
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.00001
#cosine_min_lr_ratio: 0.1
#cosine_constant_lr_ratio: 0.95

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_steps: 16
evals_per_epoch: 8
saves_per_epoch: 8
save_total_limit: 2
debug:
deepspeed:
weight_decay: 0.001
fsdp:
fsdp_config:

dpoq

This model is a fine-tuned version of Lambent/proto-nova-eidolon-v2alpha0.3-14B on the None dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 2
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 16
• training_steps: 124

Training results

Framework versions

• PEFT 0.13.2
• Transformers 4.45.2
• Pytorch 2.3.1+cu121
• Datasets 3.0.1
• Tokenizers 0.20.1