If I thought I had no idea what I was doing with quantization, I REALLY have no idea what I’m doing with LORA Fine Tuning... This works in my 10 second testing, but I have no idea beyond that, nor did I do anything other than asking it to do horrible things and seeing if it complied.
See axolotl config
axolotl version: 0.4.1
base_model: /workspace/data/models/Qwen2-7B
model_type: Qwen2ForCausalLM
tokenizer_type: Qwen2Tokenizer
trust_remote_code: true
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: NobodyExistsOnTheInternet/ToxicQAFinal
type: sharegpt
# - path: /workspace/data/SystemChat_filtered_sharegpt.jsonl
# type: sharegpt
# conversation: chatml
# - path: /workspace/data/Opus_Instruct-v2-6.5K-Filtered-v2.json
# type:
# field_system: system
# field_instruction: prompt
# field_output: response
# format: "[INST] {instruction} [/INST]"
# no_input_format: "[INST] {instruction} [/INST]"
# - path: Undi95/orthogonal-activation-steering-TOXIC
# type:
# field_instruction: goal
# field_output: target
# format: "[INST] {instruction} [/INST]"
# no_input_format: "[INST] {instruction} [/INST]"
# split: test
# - path: cognitivecomputations/WizardLM_alpaca_evol_instruct_70k_unfiltered
# type: alpaca
# split: train
dataset_prepared_path: /workspace/data/last_run_prepared
val_set_size: 0.15
output_dir: /workspace/data/outputs/Qwen2-7B-TestFinetune-LORA
chat_template: chatml
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 4
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 3e-5
train_on_inputs: false
group_by_length: true
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
warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 2
debug:
deepspeed:
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
pad_token: "<|endoftext|>"
eos_token: "<|im_end|>"
workspace/data/outputs/Qwen2-7B-TestFinetune-LORA
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0055
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: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
1.1751 | 0.0169 | 1 | 1.1860 |
1.1007 | 0.5063 | 30 | 1.0912 |
1.0418 | 1.0127 | 60 | 1.0428 |
1.0105 | 1.5042 | 90 | 1.0232 |
1.0082 | 2.0105 | 120 | 1.0127 |
0.9946 | 2.5042 | 150 | 1.0074 |
0.9826 | 3.0105 | 180 | 1.0057 |
0.9898 | 3.5021 | 210 | 1.0055 |
Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for FuturisticVibes/Qwen2-7B-TestToxicFineTune-LORA
Base model
Qwen/Qwen2-7B
Adapter
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