metadata
base_model: Qwen/Qwen2-7B
library_name: peft
tags:
- generated_from_trainer
model-index:
- name: workspace/data/outputs/Qwen2-7B-TestInstructFinetune-LORA
results: []
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 is my terrible attempt to instruct tune base Qwen2-7B, I haven't even tested this yet, I'll do that eventually...
EDIT: Tested it for a bit, seems to actually work ok, not amazing, but actually not bad, I’ll do another once I learn more about instruct tuning...
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-TestInstructFinetune-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: 4
eval_table_size:
saves_per_epoch: 4
debug:
deepspeed:
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
pad_token: "<|endoftext|>"
eos_token: "<|im_end|>"
workspace/data/outputs/Qwen2-7B-TestInstructFinetune-LORA
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5037
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 |
|---|---|---|---|
| 0.6232 | 0.0027 | 1 | 0.6296 |
| 0.5602 | 0.2499 | 91 | 0.5246 |
| 0.4773 | 0.4998 | 182 | 0.5155 |
| 0.4375 | 0.7497 | 273 | 0.5116 |
| 0.6325 | 0.9997 | 364 | 0.5092 |
| 0.4385 | 1.2382 | 455 | 0.5073 |
| 0.4949 | 1.4882 | 546 | 0.5061 |
| 0.503 | 1.7381 | 637 | 0.5052 |
| 0.5023 | 1.9880 | 728 | 0.5046 |
| 0.3737 | 2.2238 | 819 | 0.5041 |
| 0.505 | 2.4737 | 910 | 0.5039 |
| 0.4833 | 2.7237 | 1001 | 0.5038 |
| 0.4986 | 2.9736 | 1092 | 0.5037 |
| 0.5227 | 3.2108 | 1183 | 0.5037 |
| 0.5723 | 3.4607 | 1274 | 0.5037 |
| 0.4692 | 3.7106 | 1365 | 0.5037 |
| 0.5222 | 3.9605 | 1456 | 0.5037 |
Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1