You can deploy this model and start earning money today!

Fischerboot/
sophie-impvoed-v2-2epochs

Name: sophie-impvoed-v2-2epochs
Availability: InStock

peft

llama

generated_from_trainer

Fischerboot/LLama3-Lexi-Aura-3Some-SLERP-SLERP-ql-merge

adapter

4-bit

bitsandbytes

Views

No views yet

See axolotl config

axolotl version: 0.4.1

yaml

base_model: Fischerboot/LLama3-Lexi-Aura-3Some-SLERP-SLERP-ql-merge
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

chat_template: llama3
datasets:
  - path: Fischerboot/improved
    type: sharegpt
  - path: PJMixers/grimulkan_theory-of-mind-ShareGPT
    type: sharegpt
  - path: PJMixers/example-sharegpt-no-system
    type: sharegpt
  - path: PJMixers/unalignment_toxic-dpo-v0.2-ShareGPT
    type: sharegpt
    conversation: llama3
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./outputs/newandimprvoed-2ep

adapter: qlora
lora_model_dir:

sequence_len: 128
sample_packing: false
pad_to_sequence_len: true

lora_r: 8
lora_alpha: 4
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

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

loss_watchdog_threshold: 8.0
loss_watchdog_patience: 3

eval_sample_packing: false
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|begin_of_text|>"
  eos_token: "<|end_of_text|>"
  pad_token: "<|end_of_text|>"

outputs/newandimprvoed-2ep

This model is a fine-tuned version of Fischerboot/LLama3-Lexi-Aura-3Some-SLERP-SLERP-ql-merge on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.3831

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: 0.0002
train_batch_size: 1
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
7.8707	0.0043	1	5.6886
2.1474	0.2532	59	1.7117
0.9854	0.5064	118	1.5572
0.6194	0.7597	177	1.5015
0.5371	1.0129	236	1.4206
0.9501	1.2661	295	1.4110
0.6091	1.5193	354	1.4059
0.7667	1.7725	413	1.3831

Framework versions

PEFT 0.11.1
Transformers 4.41.1
Pytorch 2.1.2+cu118
Datasets 2.19.1
Tokenizers 0.19.1

Fischerboot/sophie-impvoed-v2-2epochs

outputs/newandimprvoed-2ep

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Fischerboot/
sophie-impvoed-v2-2epochs