See axolotl config
axolotl version: 0.4.1
base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
data_seed: 42
seed: 42
datasets:
- path: data/isaf_press_releases_ft.jsonl
conversation: alpaca
type: sharegpt
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/mistral/lora-out
hub_model_id: strickvl/isafpr-mistral-lora
sequence_len: 4096
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:
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
wandb_project: isaf_pr_ft
wandb_entity: strickvl
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
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: 5.0
loss_watchdog_patience: 3
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:
isafpr-mistral-lora
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0288
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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- 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 |
|---|---|---|---|
| 1.3462 | 0.0292 | 1 | 1.3536 |
| 0.1245 | 0.2628 | 9 | 0.0958 |
| 0.0521 | 0.5255 | 18 | 0.0523 |
| 0.0437 | 0.7883 | 27 | 0.0420 |
| 0.0312 | 1.0292 | 36 | 0.0383 |
| 0.0395 | 1.2920 | 45 | 0.0351 |
| 0.0309 | 1.5547 | 54 | 0.0329 |
| 0.0342 | 1.8175 | 63 | 0.0314 |
| 0.0334 | 2.0511 | 72 | 0.0318 |
| 0.0282 | 2.3139 | 81 | 0.0322 |
| 0.0263 | 2.5766 | 90 | 0.0301 |
| 0.0255 | 2.8394 | 99 | 0.0294 |
| 0.021 | 3.0803 | 108 | 0.0289 |
| 0.0236 | 3.3431 | 117 | 0.0289 |
| 0.0196 | 3.6058 | 126 | 0.0288 |
| 0.0228 | 3.8686 | 135 | 0.0288 |
Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1