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Adrastea-7b-v1.0-dpo

Text Generation
PEFT
Safetensors
mistral
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Adrastea-7b-v1.0-dpo / README.md
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metadata
license: apache-2.0
library_name: peft
tags:
  - generated_from_trainer
base_model: rishiraj/CatPPT-base
pipeline_tag: text-generation

Built with Axolotl

See axolotl config

axolotl version: 0.3.0

base_model: rishiraj/CatPPT-base
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: true
strict: false

model_config:
  output_router_logits: true

datasets:
  - path: teknium/openhermes
    type: alpaca
    prompt_style: chatml
  - path: garage-bAInd/Open-Platypus
    type: alpaca
    prompt_style: chatml
  - path: LDJnr/Capybara
    type: sharegpt
    conversation: chatml
  - path: datasets/samantha-1.1.json
    type: sharegpt
    conversation: chatml
  - path: datasets/grammarly-coedit-alpaca.jsonl
    type: alpaca
    prompt_style: chatml
  - path: datasets/dolphin-coder-codegen.jsonl
    type: alpaca_w_system.load_open_orca_chatml
  - path: datasets/dolphin-coder-translate.jsonl
    type: alpaca_w_system.load_open_orca_chatml
  - path: datasets/data-evol_instruct-decontaminated-alpaca.jsonl
    type: alpaca
    prompt_style: chatml
  - path: datasets/data-oss_instruct-decontaminated-alpaca.jsonl   
    type: alpaca
    prompt_style: chatml
  - path: jondurbin/airoboros-3.2
    type: sharegpt
    conversations: chatml
dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./qlora-out-2
seed: 420

adapter: qlora
lora_model_dir:

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save:
  - embed_tokens
  - lm_head

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 3
num_epochs: 1.5
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002

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

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

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

warmup_steps: 0
evals_per_epoch: 20
eval_table_size:
saves_per_epoch: 20
debug:
deepspeed:
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
  eos_token: "<|im_end|>"
tokens:
  - "<|im_start|>"
trust_remote_code: true

qlora-out-2

This model is a fine-tuned version of rishiraj/CatPPT-base on multiple datasets. It achieves the following results on the evaluation set:

  • Loss: 0.4258

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: 3
  • eval_batch_size: 3
  • seed: 420
  • distributed_type: multi-GPU
  • num_devices: 3
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 18
  • total_eval_batch_size: 9
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • num_epochs: 1.5

Training results

Training Loss Epoch Step Validation Loss
0.7691 0.0 1 0.7027
0.5556 0.05 135 0.5808
0.597 0.1 270 0.5488
0.5979 0.15 405 0.5277
0.4693 0.2 540 0.5108
0.5123 0.25 675 0.4978
0.4394 0.3 810 0.4883
0.46 0.35 945 0.4802
0.4472 0.4 1080 0.4748
0.47 0.45 1215 0.4687
0.4249 0.5 1350 0.4637
0.4823 0.55 1485 0.4599
0.4209 0.6 1620 0.4555
0.4909 0.65 1755 0.4517
0.4663 0.7 1890 0.4470
0.4215 0.75 2025 0.4437
0.4267 0.8 2160 0.4398
0.4109 0.85 2295 0.4364
0.4099 0.9 2430 0.4331
0.447 0.95 2565 0.4298
0.4412 1.0 2700 0.4272
0.3838 1.03 2835 0.4287
0.4262 1.08 2970 0.4274
0.3889 1.13 3105 0.4263
0.3114 1.18 3240 0.4255
0.3685 1.23 3375 0.4256
0.392 1.28 3510 0.4253
0.3751 1.33 3645 0.4255
0.3756 1.39 3780 0.4256
0.3108 1.44 3915 0.4258

Framework versions

  • Transformers 4.37.0.dev0
  • Pytorch 2.1.1+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0

Training procedure

The following bitsandbytes quantization config was used during training:

  • quant_method: bitsandbytes
  • load_in_8bit: False
  • load_in_4bit: True
  • llm_int8_threshold: 6.0
  • llm_int8_skip_modules: None
  • llm_int8_enable_fp32_cpu_offload: False
  • llm_int8_has_fp16_weight: False
  • bnb_4bit_quant_type: nf4
  • bnb_4bit_use_double_quant: True
  • bnb_4bit_compute_dtype: bfloat16

Framework versions

  • PEFT 0.6.0