See axolotl config
axolotl version: 0.4.0
base_model: mistralai/Mistral-7B-Instruct-v0.2
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: YoungPanda/chatlaw
type: sharegpt
- path: KolaGang/legal_sum
type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./pytorch
lisa_n_layers: 4
lisa_step_interval: 20
lisa_layers_attribute: model.layers
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
wandb_project: mistral_mistral
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 8
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint: out/checkpoint-99
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true
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:
save_safetensors: False
pytorch
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7778
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: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
2.6768 | 0.03 | 1 | 4.0531 |
1.521 | 0.27 | 9 | 1.3495 |
1.1368 | 0.53 | 18 | 0.9795 |
1.0257 | 0.8 | 27 | 0.8902 |
0.9861 | 1.04 | 36 | 0.8528 |
0.9431 | 1.31 | 45 | 0.8288 |
0.94 | 1.58 | 54 | 0.8070 |
0.8841 | 1.84 | 63 | 0.7938 |
0.8442 | 2.09 | 72 | 0.7851 |
0.8251 | 2.36 | 81 | 0.7808 |
0.8591 | 2.62 | 90 | 0.7783 |
0.8369 | 2.89 | 99 | 0.7778 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.0
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