metadata
base_model: EleutherAI/pythia-160m-deduped
library_name: transformers
license: apache-2.0
tags:
- axolotl
- relora
- generated_from_trainer
model-index:
- name: pythia-160m-dolphin-extended
results: []
datasets:
- cognitivecomputations/dolphin
- llamafactory/alpaca_gpt4_en
language:
- en
metrics:
- accuracy
- bleu
- rouge
See axolotl config
axolotl version: 0.4.1
base_model: EleutherAI/pythia-160m-deduped
load_in_8bit:
datasets:
- path: vicgalle/alpaca-gpt4
type: alpaca
- path: llamafactory/alpaca_gpt4_en
type: alpaca
- path: cognitivecomputations/dolphin
name: flan1m-alpaca-uncensored
type: alpaca
shards: 10
dataset_prepared_path: ds-mega-alpaca
#dataset_shard_num: 10
chat_template: inst
val_set_size: 0.001
adapter: lora
lora_model_dir:
sequence_len: 2048
lora_r: 16
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
- query_key_value
lora_target_linear:
lora_fan_in_fan_out: true # pythia/GPTNeoX lora specific
lora_modules_to_save:
- embed_in
- embed_out
- lm_head
lora_on_cpu: false
# ReLoRA configuration
# # Must use either 'lora' or 'qlora' adapter, and does not support fsdp or deepspeed
# relora_steps: # Number of steps per ReLoRA restart
# relora_warmup_steps: # Number of per-restart warmup steps
# relora_anneal_steps: # Number of anneal steps for each relora cycle
# relora_prune_ratio: # threshold for optimizer magnitude when pruning
# relora_cpu_offload: # True to perform lora weight merges on cpu during restarts, for modest gpu memory savings
relora_steps: 600
relora_warmup_steps: 10
relora_cpu_offload: true
wandb_project: pythia
wandb_entity:
wandb_watch:
wandb_name: pythia-160m-dolphin-extended
wandb_log_model:
output_dir: ./outputs/lora-alpaca-pythia-160m-dolphin-extended
gradient_accumulation_steps: 16
micro_batch_size: 1
num_epochs: 1
learning_rate: 0.0004
lr_scheduler: cosine_with_restarts
#cosine_min_lr_ratio: 0.1
train_on_inputs: false
group_by_length: false
#bf16: auto
#fp16: true
#tf32: false
float16: true
flash_attn:
xformers_attention: true
optimizer: paged_adamw_8bit
gpu_memory_limit: 8GiB
hub_model_id: jtatman/pythia-160m-dolphin-extended
early_stopping_patience: 10
#resume_from_checkpoint: outputs/lora-alpaca-pythia-160m-dolphin-extended/checkpoint-11400
auto_resume_from_checkpoints: true
local_rank:
weight_decay: 0.0
#evals_per_epoch: 4
eval_steps: 200
logging_steps: 1
save_steps: 200
save_total_limit: 5
warmup_steps: 100
tokens:
- "[INST]"
- "[/INST]"
pythia-160m-dolphin-extended
This model is a fine-tuned version of EleutherAI/pythia-160m-deduped on the None dataset. It achieves the following results on the evaluation set:
- Loss: 6.6729
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: 0.0004
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 25.9906 | 0.0001 | 1 | 29.5342 |
| 21.1303 | 0.0167 | 200 | 20.2350 |
| 16.5026 | 0.0334 | 400 | 18.4930 |
| 17.2725 | 0.0500 | 600 | 16.3395 |
| 11.9697 | 0.0667 | 800 | 12.1401 |
| 11.3783 | 0.0834 | 1000 | 11.8383 |
| 12.8084 | 0.1001 | 1200 | 12.9667 |
| 9.4119 | 0.1167 | 1400 | 9.8787 |
| 10.3527 | 0.1334 | 1600 | 10.0560 |
| 9.3545 | 0.1501 | 1800 | 9.7355 |
| 8.9165 | 0.1668 | 2000 | 9.1513 |
| 8.5467 | 0.1835 | 2200 | 8.2025 |
| 7.9152 | 0.2001 | 2400 | 7.6616 |
| 7.3362 | 0.2168 | 2600 | 7.5699 |
| 7.9374 | 0.2335 | 2800 | 7.4818 |
| 7.838 | 0.2502 | 3000 | 7.4635 |
| 7.5731 | 0.2668 | 3200 | 7.4899 |
| 7.8289 | 0.2835 | 3400 | 7.3594 |
| 7.8906 | 0.3002 | 3600 | 8.0934 |
| 7.7318 | 0.3169 | 3800 | 7.5812 |
| 7.2089 | 0.3335 | 4000 | 7.4839 |
| 7.202 | 0.3502 | 4200 | 7.4486 |
| 6.9493 | 0.3669 | 4400 | 7.3208 |
| 7.1492 | 0.3836 | 4600 | 7.2469 |
| 7.3443 | 0.4003 | 4800 | 7.1378 |
| 7.7056 | 0.4169 | 5000 | 7.1385 |
| 55.0553 | 0.4336 | 5200 | 50.0135 |
| 7.1868 | 0.4503 | 5400 | 6.9898 |
| 6.5803 | 0.4670 | 5600 | 6.9559 |
| 8.6171 | 0.4836 | 5800 | 7.9075 |
| 7.1373 | 0.5003 | 6000 | 6.9280 |
| 6.7077 | 0.5170 | 6200 | 6.8797 |
| 7.0026 | 0.5337 | 6400 | 6.8635 |
| 6.6797 | 0.5504 | 6600 | 6.8178 |
| 6.8067 | 0.5670 | 6800 | 6.7893 |
| 6.5979 | 0.5837 | 7000 | 6.8106 |
| 6.7283 | 0.6004 | 7200 | 6.7998 |
| 7.0015 | 0.6171 | 7400 | 6.7705 |
| 6.1182 | 0.6337 | 7600 | 6.7592 |
| 6.7919 | 0.6504 | 7800 | 6.7446 |
| 6.4523 | 0.6671 | 8000 | 6.7260 |
| 6.765 | 0.6838 | 8200 | 6.7135 |
| 6.4625 | 0.7004 | 8400 | 6.7099 |
| 6.79 | 0.7171 | 8600 | 6.7070 |
| 6.6101 | 0.7338 | 8800 | 6.7017 |
| 6.7541 | 0.7505 | 9000 | 6.6964 |
| 6.7777 | 0.7672 | 9200 | 6.6901 |
| 7.2082 | 0.7838 | 9400 | 6.6869 |
| 6.4263 | 0.8005 | 9600 | 6.6875 |
| 6.1944 | 0.8172 | 9800 | 6.6803 |
| 6.7745 | 0.8339 | 10000 | 6.6865 |
| 6.6746 | 0.8505 | 10200 | 6.6756 |
| 6.6319 | 0.8672 | 10400 | 6.6941 |
| 6.6657 | 0.8839 | 10600 | 6.6764 |
| 6.8516 | 0.9006 | 10800 | 6.6776 |
| 6.6391 | 0.9173 | 11000 | 6.6749 |
| 6.5763 | 0.9339 | 11200 | 6.6729 |
| 6.585 | 0.9506 | 11400 | 6.6694 |
| 6.2999 | 0.9673 | 11600 | 6.6722 |
| 6.8343 | 0.9840 | 11800 | 6.6729 |
Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
Evaluation Results
| Groups | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| Open LLM Leaderboard | N/A | none | 5 | rouge2_max | 16.4873 | ± | 1.0172 |
| - winogrande | 1 | none | 5 | acc | 0.5120 | ± | 0.0224 |
| - gsm8k | 3 | strict-match | 5 | exact_match | 0.0060 | ± | 0.0035 |
| - hellaswag | 1 | none | 10 | acc | 0.3520 | ± | 0.0214 |
| - mmlu | N/A | none | 0 | acc | 0.2533 | ± | 0.0039 |
| none | 5 | rouge2_acc | 0.1920 | ± | 0.0176 | ||
| none | 5 | rougeL_acc | 0.3860 | ± | 0.0218 | ||
| flexible-extract | 5 | exact_match | 0.0220 | ± | 0.0066 | ||
| strict-match | 5 | exact_match | 0.0060 | ± | 0.0035 | ||
| none | 5 | rougeL_diff | -0.7765 | ± | 1.0034 | ||
| none | 5 | rouge1_acc | 0.3700 | ± | 0.0216 | ||
| none | 5 | rouge1_diff | -1.5564 | ± | 1.0223 | ||
| none | 5 | acc_norm | 0.3180 | ± | 0.0145 | ||
| none | 5 | bleu_diff | -0.6500 | ± | 0.6421 | ||
| none | 5 | rouge1_max | 36.3550 | ± | 0.9462 | ||
| none | 5 | acc | 0.2664 | ± | 0.0036 | ||
| none | 5 | rougeL_max | 33.8798 | ± | 0.9367 | ||
| none | 5 | bleu_max | 15.2292 | ± | 0.6714 | ||
| none | 5 | bleu_acc | 0.4360 | ± | 0.0222 | ||
| none | 5 | rouge2_diff | -3.3178 | ± | 0.9477 | ||
| - mmlu | N/A | none | 0 | acc | 0.2533 | ± | 0.0039 |
| - humanities | N/A | none | 5 | acc | 0.2408 | ± | 0.0075 |
| - other | N/A | none | 5 | acc | 0.2443 | ± | 0.0080 |
| - social_sciences | N/A | none | 5 | acc | 0.2538 | ± | 0.0081 |
| - stem | N/A | none | 5 | acc | 0.2740 | ± | 0.0079 |
| - truthfulqa | N/A | none | 0 | rouge2_max | 16.4873 | ± | 1.0172 |
| none | 0 | rouge2_acc | 0.1920 | ± | 0.0176 | ||
| none | 0 | rougeL_acc | 0.3860 | ± | 0.0218 | ||
| none | 0 | rougeL_diff | -0.7765 | ± | 1.0034 | ||
| none | 0 | rouge1_acc | 0.3700 | ± | 0.0216 | ||
| none | 0 | rouge1_diff | -1.5564 | ± | 1.0223 | ||
| none | 0 | bleu_diff | -0.6500 | ± | 0.6421 | ||
| none | 0 | rouge1_max | 36.3550 | ± | 0.9462 | ||
| none | 0 | acc | 0.3435 | ± | 0.0137 | ||
| none | 0 | rougeL_max | 33.8798 | ± | 0.9367 | ||
| none | 0 | bleu_max | 15.2292 | ± | 0.6714 | ||
| none | 0 | bleu_acc | 0.4360 | ± | 0.0222 | ||
| none | 0 | rouge2_diff | -3.3178 | ± | 0.9477 |